Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based 3D Reconstruction

arXiv:2509.24867 · cs.RO · Submitted 2025-09-29 · Read on arXiv

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Introduction to the show: ident: Robotics Radio. Generated commentary on the latest robotics and control papers.

Rosa: Today's paper: "Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based 3D Reconstruction".

Dev: The gist:

Rosa: First, who's behind it and why it matters.

Title and authors: Rosa: So we’re looking at this paper, "Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based three dee Reconstruction," and it tackles how to automate the very first step of setting up a robotic ultrasound probe <ref:2509.24867#pg1,Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR>. It seems like they’re trying to solve that huge problem where expert operators spend too much time just getting the probe into the right spot before they can even start imaging Rosa.

Dev: Yeah, it’s about taking that manual setup—that initial positioning of the probe on a patient's chest—and replacing it with some kind of automated system. It addresses the issue that current methods, especially those using just a camera, are really sensitive to things like lighting and how variable the patient anatomy is Dev.

Taro: I mean, that’s what I’m interested in from an autonomy standpoint. If you can automate that initial placement, it opens up a whole new way for remote procedures to work without needing a highly trained person right there at the console Taro.

Rosa: Exactly. The paper proposes using a robot-mounted 2D LiDAR scanner to reconstruct the chest surface in three dimensions, and then use that shape to estimate where the probe should go Rosa <ref:2509.24867#pg1,a robot-mounted 2D LiDAR>. It’s essentially building a three dee map of the patient's chest front so the system knows where to start its search for the probe <ref:2509.24867#pg1>.

Dev: And they get their data from a UR5e collaborative robot—that’s a common platform, which is good because it means they have some built-in safety features that keep things controlled while you’re operating remotely Dev. They also use a Clarius C3 HD3 ultrasound probe mounted on it to make sure the physical setup doesn't mess up their method Dev.

Taro: So, what’s the actual process? I want to know how they handle getting that LiDAR data from the robot into a usable three dee model of the patient’s body <ref:2509.24867#pg1>. That’s where I get curious about their autonomy choices Taro.

Rosa: They use a plane-based extrinsic calibration strategy for that, which is pretty smart because they don't need fancy external measuring tools or fiducials to figure out how the LiDAR sensor relates to the robot arm Rosa. They set up a flat board, maybe six hundred by nine hundred millimeters, and they sweep the LiDAR across it multiple times to estimate that transformation between the sensor and the robot’s base frame Rosa <ref:2509.24867#pg3>.

Title and authors: Dev: Minimizing point-to-plane residuals is a standard way to do that calibration without needing external metrology, which keeps their setup relatively simple for a lab environment Dev. They are trying to get an overall root mean square residual of one point eight two millimeters in translation and rotational uncertainty below zero point two degrees, which they found pretty good for the transformation between the LiDAR and robot frames Dev.

Taro: That level of accuracy on the calibration itself is important because if the base map is off by even a little bit, all that subsequent reconstruction will be flawed Taro. Does this method work well when you move it outside a controlled lab setting? I mean, Rosa wants to know about real-world deployment Taro.

Rosa: That’s the big question for me. The paper shows they validated the reconstruction accuracy on mannequins, finding mean surface errors of two point seven eight plus or minus zero point two one millimeters when comparing their model to the actual shape of the mannequin Rosa. But then they moved to human trials, and that's where things got a bit rougher Rosa.

Dev: Yeah, the human trials showed that probe initial points typically ended up twenty to thirty millimeters away from what clinicians define as the ideal starting point for them Dev. That’s a pretty big error margin if you’re trying to be precise, but they noted that variation across repeated trials on the same subject was less than four millimeters Dev.

Taro: So it seems like the system isn't perfect for finding the absolute center of the chest immediately; it gets within a couple of centimeters. What about what that means for someone just watching this show, like I’m driving home and trying to figure out if this is something practical right now? Taro.

Rosa: For you, it suggests that coarse placement errors are operationally acceptable because routine echocardiography often involves sliding and minor probe adjustments anyway Rosa. The finding in the paper is that this LiDAR-based method provides a consistent starting point, even if it’s not perfectly exact right out of the box Rosa.

Dev: And they did incorporate a non-rigid template matching framework to actually estimate the probe's initial pose after they got that three dee reconstruction done Dev <ref:2509.24867#pg1>. That part is what really helps them adapt to different people because it uses predefined chest surface templates for males and females, scaled up or down by ten percent to handle inter-subject variability Dev.

Title and authors: Taro: That scaling mechanism sounds like a good way to handle the natural differences between patients without needing an entirely new model for every single person Taro. So they aren't just matching one perfect template; they are testing different versions of it against the reconstructed shape Taro.

Rosa: Right. And once they find an alignment fitness score of ninety percent or higher, they map the ideal probe point from that template onto the nearest point on their reconstructed chest cloud, which gives them that initial probe position Rosa. It’s a multi-step process to get there Rosa.

Dev: From my side, I see this as a solid pipeline. You have the raw LiDAR data, you calibrate it using the plane strategy with those low residuals, you build the three dee cloud, and then you feed that into the matching framework to get a usable pose estimate Dev <ref:2509.24867#pg1>. The latency here is managed by having them use fast global registration before doing the more intensive ICP refinement Dev.

Taro: I wonder what happens if something goes wrong in that sequence. If the LiDAR sweep isn't perfect, or if the template matching fails to find a good fit, does the robot just stop and wait? What’s their plan for when things go sideways? Taro.

Rosa: They have safety measures built in from the start, like constraining the robot’s tool center point speed and joint limits in software, plus they define a virtual safety plane to prevent motion toward the probe with holder Rosa. They also have that emergency stop button for immediate interruption if there’s any discomfort during operation Rosa.

Dev: That hardware and software safety layering is what makes this work in a teleoperated setting rather than just some open-ended robot movement Dev. It keeps the loop rate and the latency manageable while giving operators control over the physical movement of the robot Dev.

Taro: So, to bring it back to you listening right now, what does this actually mean for someone who doesn't work with robots or ultrasound? Does it make cardiac imaging any easier in a real hospital setting? Taro.

Rosa: It means that the initial setup of the robotic probe is becoming automated, which reduces the manual workload for the technician running the remote procedure Rosa. It’s a step toward making these procedures less dependent on having an expert physically maneuvering everything from scratch Rosa.

Dev: The implication is that you could potentially cut down on preparation time significantly, which helps reduce diagnostic delays in critical care scenarios where speed matters most Dev. But we have to remember the caveat they mentioned about the accuracy in human trials—that twenty to thirty millimeter positional error range Dev.

Title and authors: Taro: That positional error is what I wanted to hear more about. If you’re getting twenty millimeters off, that’s not zero error; it’s a definite adjustment needed by the operator, right <ref:2509.24867#pg2>? Taro.

Rosa: Exactly. It means the system gives you a very good first guess, but the final placement still relies on clinical judgment and those minor adjustments during the teleoperation process Rosa. They aren't claiming perfect placement yet; they are aiming for a clinically meaningful initial pose Rosa.

Dev: And that’s where the work seems to land. They’ve demonstrated a robust way to create a consistent three dee model of the chest surface using LiDAR, and then used template matching to provide an orientation aligned with local surface normals, which is what they call PCA-based orientation estimation Dev <ref:2509.24867#pg1>.

Taro: That local surface normal thing sounds like it’s trying to give the probe an anatomically neutral starting angle so the operator doesn't have to worry about that detail right away Taro.

Rosa: Right. And they did show that this overall LiDAR-based method is consistent and robust for initial pose estimation, which is a huge step forward in getting these robotic setups ready for actual clinical use Rosa.

Dev: So, we’ve seen the methodology, the accuracy numbers—like that one point eight two millimeter RMS residual during calibration—and the specific steps they take to match templates Dev <ref:2509.24867#pg1>. It’s a lot of engineering going into that pipeline before you even get to imaging Dev.

Taro: I just want to make sure we don't forget the limitations. What is the biggest thing they admit about this system not doing? What’s the gap between what they built and what a doctor needs right now? Taro.

Rosa: They do point out that their reliance on depth sensing in previous vision-based approaches can be vulnerable to ambient light, which is why they chose LiDAR—range measurements are based on time-of-flight and are invariant to lighting changes Rosa. That’s the trade-off they made by choosing this specific sensor Rosa.

Dev: And they also note that their method still requires two linear sweeps across the patient’s chest front surface to get a consistent three dee model, which adds some time to the initialization routine compared to a single scan Dev <ref:2509.24867#pg1>. That’s a known trade-off in achieving better geometric fidelity Dev.

Title and authors: Taro: So, it’s not just about getting the shape right; it’s about balancing how much computational effort you put into getting that shape versus how long the setup takes overall Taro. It sounds like they are trying to find that balance Taro.

Rosa: That’s the core theme. They showed a way to automate initialization using 2D LiDAR, and they established a plane-based calibration that gives them solid geometric accuracy before they even start matching templates Rosa <ref:2509.24867#pg1>. It’s a big step in moving teleoperated echocardiography toward being less operator-dependent Rosa.

Dev: This paper, "Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based three dee Reconstruction," gives us the blueprint for using range sensors to map complex surfaces and then using that map to guide the placement of a tool with high precision Dev <ref:2509.24867#pg1,Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR>. The results show that even with human variability, you get an initial pose estimate that is operationally acceptable for routine use Dev.

Taro: For those of us thinking about future autonomous systems, this shows a clear path: build the geometric understanding first, and then layer on the matching framework to handle the messy biological reality Taro. It’s a good foundation for more complex autonomous tasks down the line Taro.

Rosa: We’re wrapping up our thoughts on this paper. It’s a solid demonstration of using LiDAR to reconstruct a human body surface and automatically estimating an initial probe pose in teleoperated echocardiography Rosa. The authors achieved extrinsic accuracy of less than two millimeters in translation and under zero point two degrees rotation, followed by consistent three dee models from two linear LiDAR sweeps Rosa.

Dev: And they used the non-rigid template matching to localize that clinically meaningful initial probe pose after preprocessing the point cloud with voxel downsampling and clustering Dev. That’s a complete workflow from sensor to final pose estimate Dev.

Taro: It really shows how you can use geometry, calibration, and then statistical matching to tackle a very practical, real-world problem in medicine without needing some super complex deep learning model just for the initial step Taro.

Rosa: That’s the gist of it. The paper has set a new baseline for automating that tedious first step in robotic imaging Rosa. We'll be looking at what they do next, how they plan to extend this beyond the lab, and what clinical trials look like down the road Rosa.

Dev: Next up, we’re going to look at some other recent work on control and learning systems that are tackling uncertainty in complex environments. Stay with us Dev.

The paper's summary: Rosa: So, this paper is about using a robot's 2D LiDAR scanner to build a three-dimensional map of a patient’s chest surface, and then using that map to automatically figure out exactly where the ultrasound probe should go before we even start imaging remotely <ref:2509.24867#pg1>.

Dev: Yeah, they use this whole pipeline—from calibrating the sensor against a flat board to doing two sweeps across the chest—to create that three dee model. It’s all about automating that very first physical setup step for teleoperated echocardiography.

Rosa: What they did really interesting is how they handle the matching part after they build the shape. They use a non-rigid template matching framework, which means they aren't just looking for an exact match; they let their system try out different versions of male and female chest shapes, scaled up or down by ten percent to account for how much people vary.

Dev: That scaling is key because it shows the authors are trying to handle inter-subject variability, meaning the system adapts to different body sizes without needing a completely new model for every patient. They’re aiming for a ninety percent alignment fitness score before they finalize the probe's initial position.

Rosa: The results showed that when they tested this on mannequins, their reconstruction accuracy was within about three millimeters of the actual surface shape. That’s pretty good geometric fidelity for something done in a lab setting.

Dev: But then they moved it to humans, and that’s where things got a little more realistic. They found that the initial probe point ended up roughly twenty to thirty millimeters away from where clinicians usually place it.

Rosa: So, what does that actually mean for someone just listening? It means this system gives you a really good first guess—a starting point—but it’s not pinpoint accurate enough for immediate clinical use without some human fine-tuning during the remote operation.

Dev: Exactly. The authors themselves say that coarse placement errors are acceptable because routine echocardiography involves sliding the probe and making minor adjustments anyway. Their goal isn't perfect placement right away; it's getting a clinically meaningful starting orientation that reduces the operator’s workload when they start the procedure.

Rosa: It sounds like they proved that you can use 2D LiDAR to create a robust, consistent three dee surface model and then use statistical matching to find an initial pose, which is a big step toward automating the tedious prep work in remote imaging <ref:2509.24867#pg1>.

Dev: It’s a solid demonstration of how you can combine geometric calibration with non-rigid template matching to automate initialization in robotic medical procedures. Now we need to see how they plan to move this from a lab demonstration into something that actually runs reliably outside of a controlled environment, and that’s what we're going to look at next.

The paper's improvements: Rosa: So, we’re looking at how they plan to take this initial system and make it work better in the real world, right? They didn't just stop at getting a rough estimate; they put some new ideas in there for future steps.

Dev: Yeah, after they established that geometric accuracy with that plane-based calibration, their focus shifted to making sure the probe’s final orientation is actually useful for clinical work. That’s where the non-rigid template matching comes in again, but this time it uses something called PCA, which means they’re looking at the surface normals of those nearest points to figure out a neutral starting angle.

Rosa: So they're trying to give the probe an anatomically neutral orientation, something that helps the operator not have to worry about that detail right away when they start adjusting things remotely. That makes sense because if you can set it up in a good starting position, the operator’s job is easier.

Dev: It’s a refinement step for autonomy. They’re using this orientation estimation to guide the next phase of movement, which is why it's important—it moves beyond just finding *a* place to put the probe to finding an *ideal* starting angle for the imaging sequence.

Rosa: And they mentioned planning some future work, which is where things get really interesting for me as a field roboticist. They plan to optimize those LiDAR scanning trajectories dynamically based on how big the patient is, not just sweeping over a fixed board.

Dev: That’s smart because if you know the patient’s size beforehand, you can adjust your scanning path so you don't waste time or get redundant data points, which would help keep the loop rate stable during the reconstruction phase. It’s about making the process more efficient.

Rosa: And then there are plans for defining strategies for safe and efficient motion. They want to figure out how to move smoothly from a resting position to that identified ideal probe pose without hitting any safety limits or causing jerky movements that could mess up the imaging session.

Dev: That safety aspect is crucial because as the system gets more autonomous, you need robust transition planning so the robot doesn't just stop dead when it gets close to the target. It needs to know how to handle that approach smoothly while still respecting all those joint limits we talked about earlier.

Rosa: It sounds like they’re moving from just a successful lab demonstration toward creating a truly adaptive system that can handle different patients and different physical sizes in the field. That’s where I see the biggest practical impact for clinical settings down the road.

Dev: Exactly, it shows how you build on that initial geometric foundation to create something more resilient against real-world biological variation and operational constraints. We need to see those adaptive trajectories working in action before we can really say this is ready for widespread deployment Dev.

Conclusion: Rosa: So, to wrap up, this paper on "Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based three dee Reconstruction" shows how you can use a robot scanner and some smart matching to build a usable three dee model of a patient’s chest surface automatically <ref:2509.24867#pg1,Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR>.

Dev: Right, it establishes that the plane-based calibration gives you that solid geometric foundation with very low residuals, which is crucial for making sure the transformation between the sensor and the robot base frame is accurate.

Rosa: And they showed that even though it’s not perfect in human trials, getting an initial pose within twenty to thirty millimeters of where a doctor would place it is operationally acceptable for routine use. That’s good news for reducing operator workload in remote procedures.

Dev: It means the system is providing a consistent starting point, which makes the whole process less dependent on a highly trained person just maneuvering everything from scratch before they start imaging.

Taro: I think what stands out is how they handle those initial errors; it shows that even with human anatomical variation, the method still provides an alignment fitness score high enough to select a useful template.

Rosa: That’s exactly what I thought—it’s not about perfect placement right away, but about getting a good starting guess so the operator can finish their job efficiently.

Dev: And they pointed out that future work will involve making those LiDAR scanning trajectories adaptive to patient size, which is the next logical engineering step for real-world robustness.

Taro: I’m still curious about those safety measures, though. When things go wrong during that trajectory planning, how does the system handle unexpected obstacles or sudden changes in the robot's environment?

Rosa: That’s a big question for me. They did mention constraints on tool center point speed and joint limits in software, plus they defined a virtual safety plane to prevent motion toward the probe holder. Those are the basic safety layers built into the system.

Dev: Yeah, those hardware and software safety layering is what keeps this work manageable in a teleoperated setting rather than just some open-ended robot movement. It’s about keeping the loop rate stable while giving operators control over the physical movement of the robot.

Rosa: So, we’ve seen how they used LiDAR to reconstruct a chest surface and then used template matching to locate a clinically meaningful starting pose for teleoperated echocardiography.

Dev: It's a solid demonstration of combining geometric calibration with non-rigid template matching to automate initialization in robotic medical procedures.

Taro: It really shows you can use geometry, calibration, and statistical matching to tackle that very practical problem in medicine without needing some super complex deep learning model just for the initial setup.

Rosa: That’s the gist of it. The paper has set a new baseline for automating that tedious first step in robotic imaging by using 2D LiDAR to map surfaces and then guiding probe placement automatically <ref:2509.24867#pg1>.

Dev: We'll be looking at what they do next, how they plan to extend this beyond the lab, and what kind of clinical trials look like down the road.

School of Engineering, Swinburne University of Technology

cs.RO

Submitted: 2025-09-29

Updated: 2025-09-29

Comments: This work has been submitted to the IEEE for possible publication

Journal ref: 2026 IEEE International Conference on Robotics and Automation (ICRA), 2026

DOI: 10.1109/ICRA57385.2026.11697435

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 78/100

The gist: The gist: This work presents the first demonstration of reconstructing a human body surface in 3D using a robot-mounted 2D LiDAR scanner to automate initial probe pose estimation in teleoperated

Key concepts

Robot-Mounted 2D LiDAR: This is a system using a robot equipped with a 2D laser scanner (LiDAR) to map the environment. The robot moves this scanner around the patient's chest to create detailed, real-time 3D maps of the surface. This provides precise geometric data about where the probe needs to be placed.
Plane-Based Extrinsic Calibration: This technique is used to accurately determine how the LiDAR scanner relates spatially to the robot's base frame. Instead of relying on external markers, researchers used a flat board and measured how the LiDAR points matched that known plane. This calibration ensures that the 3D map generated by the LiDAR is precisely oriented relative to where the robot's arm is located.
Non-rigid Template Matching: This involves comparing the newly created 3D chest surface reconstruction with pre-existing, idealized 3D models of human chests. The system finds the best fit between these two surfaces, accounting for natural variations in body shape and size. This allows the robot to automatically identify the correct starting point for placing the ultrasound probe.

Terminology

Accuracy — 1× in this paper · explained in 1 episode
Calibration — 1× in this paper · explained in 7 episodes

Summary

The gist: This work presents the first demonstration of reconstructing a human body surface in 3D using a robot-mounted 2D LiDAR scanner to automate initial probe pose estimation in teleoperated echocardiography.

System Overview

The core platform for this study is a UR5e collaborative robot (cobot), chosen for its compliance and built-in safety features <ref:2509.24867#pg2>. A Clarius C3 HD3 wireless ultrasound probe was mounted to emulate a realistic clinical setup and verify that the probe geometry did not interfere with the proposed approach <ref:2509.24867#pg2>. The control architecture and data flow were implemented in Robot Operating System (ROS) 1 <ref:2509.24867#pg2>. Safety measures include constraining the robot’s tool center point (TCP) speed and joint limits in software, defining a virtual safety plane to prevent motion toward the Probe with holder <ref:2509.24867#pg2>.

Plane-Based LiDAR–TCP Extrinsic Calibration and Chest Surface 3D Reconstruction

The method for mapping LiDAR points into the fixed robot base frame involves estimating the extrinsic transformation C TL between the LiDAR and TCP frames <ref:2509.24867#pg2>. This is achieved by adopting a planar constraint strategy where a 600 mm × 900 mm flat board is placed within reach and the robot sweeps the LiDAR across it at multiple poses <ref:2509.24867#pg2>. The extrinsics are estimated by minimizing point to plane residuals, which avoids external metrology and fiducials <ref:2509.24867#pg2>. The calibration setup involves defining a raw range return as pL(r, θ) = [r cos θ; r sin θ; 0] <ref:2509.24867#pg2>. A RANSAC-based 2D line fitting procedure is applied to extract the dominant line corresponding to the intersection of the LiDAR scan plane with the calibration board <ref:2509.24867#pg2>. The transformation is then formulated as a robust nonlinear leastsquares problem min ξ X K k=1 X i∈Ik ρ nB(v)⊤x(k)B,i(ω, t) + dB, which allows for the estimation of C TL <ref:2509.24867#pg2>.

Non-rigid Template Matching Framework for Initial Probe Pose Estimation

Following 3D reconstruction, a non-rigid template matching framework is applied to automatically estimate the probe’s initial pose <ref:2509.24867#pg2>. The reconstructed 3D point cloud is first preprocessed using voxel downsampling to ensure uniform point density, followed by statistical and radius outlier removal, density-based clustering, and Poisson surface reconstruction with signed-distance trimming <ref:2509.24867#pg2>. Predefined chest surface templates in the form of point clouds (male and female variants), each annotated with an ideal initial probe point pT ∈ R3, were employed as references <ref:2509.24867#pg2>. To accommodate inter-subject variability, each template was scaled about its centroid to generate three variants: the original size, +10%, and-10% <ref:2509.24867#pg2>. Fast global registration (FGR) followed by iterative closest point (ICP) refinement is applied to obtain correspondences <ref:2509.24867#pg2>. If any variant achieved an alignment fitness ≥90%, that result was selected, resulting in the aligned template and preprocessed point cloud <ref:2509.24867#pg2>. After alignment, the template point pT is mapped to its corresponding point pS on the preprocessed point cloud, identified as the nearest neighbor to pT, yielding the initial probe position <ref:2509.24867#pg2>.

Evaluations

Quantitative validation on mannequins confirmed reconstruction accuracy within 3 mm RMSE and. In human trials, the tangential positional error e∥ across all participants and trials ranged from 21–30 mm, with mean values clustering in the mid–20 mm range <ref:2509.24867#pg2>. This suggests that coarse placement errors are operationally acceptable since routine echocardiography relies on sliding and minor probe adjustments <ref:2509.24867#pg2>. The findings indicate that the LiDAR-based method provides a consistent and robust initial probe pose estimation, with initialization errors well within the anatomical variability encountered in clinical echocardiography <ref:2509.24867#pg2>.

Conclusion and Future Work

This work presented the first demonstration of using a robot-mounted 2D LiDAR to automate probe pose initialization in teleoperated echocardiography <ref:2509.24867#pg2>. A plane-based calibration strategy enabled extrinsic accuracy of less than 2 mm in translation and under 0.2◦ rotation, after which two linear sweeps for LiDAR reconstruction produced consistent 3D models of the chest front surface <ref:2509.24867#pg2>. A non-rigid templatematching framework with sex-specific chest front surface priors and PCA-based orientation estimation then localized a clinically meaningful initial probe pose for placement <ref:2509.24867#pg2>. Future work will extend this foundation by optimizing LiDAR scanning trajectories adaptively to patient size, defining strategies for safe and efficient motion from a rest position to the identified probe initial pose, and planning a full clinical study <ref:2509.24867#pg2>.

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This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible <ref:2509.24867#pg2>. Finding an Initial Probe Pose in Teleoperated Robotic Echocardiography via 2D LiDAR-Based 3D Reconstruction Mariadas Capsran Roshan1, Edgar M Hidalgo1, Mats Isakssson1, Michelle Dunn1, Jagannatha Charjee Pyaraka1 Abstract— Echocardiography is a key imaging modality for cardiac assessment but remains highly operator-dependent <ref:2509.24867#pg5>. Teleoperated robotic echocardiography has been proposed as a solution <ref:2509.24867#pg6>. Automating non-expert tasks, such as automatically moving the probe to an ideal starting pose, offers a pathway to reduce this burden <ref:2509.24867#pg6>. Prior visionand depth-based approaches to estimate an initial probe pose are sensitive to lighting, texture, and anatomical variability <ref:2509.24867#pg7>. We propose a robot-mounted 2D LiDAR-based approach that reconstructs the chest surface in 3D and estimates the initial probe pose automatically <ref:2509.24867#pg8>. To the best of our knowledge, this is the first demonstration of robot-mounted 2D LiDAR used for 3D reconstruction of a human body surface. Through planebased extrinsic calibration, the transformation between the LiDAR and robot base frames was estimated with an overall root mean square (RMS) residual of 1.82 mm and rotational uncertainty below 0.2◦. The chest front surface, reconstructed from two linear LiDAR sweeps, was aligned with non-rigid templates to identify an initial probe pose <ref:2509.24867#pg2>. Mannequin-based study assessing reconstruction accuracy showed mean surface errors of 2.78±0.21 mm <ref:2509.24867#pg2>. Human trials (N=5) evaluating the proposed approach found probe initial points typically 20–30 mm from the clinically defined initial point, while the variation across repeated trials on the same subject was less than 4 mm <ref:2509.24867#pg2>.

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I. INTRODUCTION Echocardiography is the application of ultrasound to visualize the heart, providing real-time assessment of cardiac anatomy and function <ref:2509.24867#pg2>. It is a primary diagnostic tool for detecting heart failure, evaluating valvular disorders, identifying congenital abnormalities, and guiding decisionmaking in emergency and critical-care contexts [1], [2] <ref:2509.24867#pg2>. The accuracy of echocardiography is highly operator-dependent, and access to trained sonographers is limited in regional and underserved areas [3] <ref:2509.24867#pg2>. Delays in cardiac assessment worsen outcomes, with one study [4] reporting that in acute heart attack patients each additional 30-minute emergency medical transport delay increased the 30-day mortality risk by 20% <ref:2509.24867#pg2>. Teleoperated robotic echocardiography [5] has been proposed as a promising solution to overcome this limitation <ref:2509.24867#pg2>. Such a solution includes a robot equipped with an ultra-sound probe is manipulated remotely by an echocardiographer, with real-time feedback enabling accurate image acquisition across distance <ref:2509.24867#pg2>. A clinical study [6] demonstrated that this approach can reduce diagnostic delays by nearly three months compared to conventional referral pathways that often require patients to travel to specialized facilities <ref:2509.24867#pg2>. However, as summarized in Table I, multiple clinical studies report longer durations for teleoperated robot echocardiography compared to manual examinations <ref:2509.24867#pg2>. Previous research [10], [11] has proposed that not all stages of echocardiography require expert input and that repetitive preparatory steps can be automated to reduce examination time <ref:2509.24867#pg2>. One such step is the initial placement of the probe on the chest above the heart, which currently requires manual maneuvering from a neutral position <ref:2509.24867#pg2>. Automating this sequence can reduce procedure time and operator workload <ref:2509.24867#pg2>. Several studies have investigated vision-based approaches to automate initial probe pose estimation within teleoperated and other robot-assisted ultrasound imaging systems, spanning applications in both echocardiography and other anatomical targets <ref:2509.24867#pg2>. Mustafa et al. [12] targeted the epigastric region by processing a frontal RGB photograph of the torso, automatically detecting anatomical landmarks such as the umbilicus and nipples, and then applying anthropometric distance ratios to triangulate an approximate target position for probe placement <ref:2509.24867#pg2>. In contrast, Ning et al. [13] avoided landmarks through a reinforcement learning framework <ref:2509.24867#pg2>.

Improvements for AI systems

Confidence in pose estimation is enhanced by using non-rigid template matching framework to estimate initial probe pose, which allows for adaptation to inter-subject variability via scaling variants (the original size, +10%, and-10%). This enables the system to find a suitable alignment even when anatomical features vary significantly.
Robust extrinsic calibration is achieved through plane-based extrinsic calibration, which minimizes residuals to an overall root mean square (RMS) residual of 1.82 mm and rotational uncertainty below 0.2◦. This ensures that the transformation between the LiDAR and robot base frames is highly accurate, providing a stable geometric foundation for all subsequent reconstruction steps.
Privacy-preserving spatial encoding is integrated by relying on LiDAR measurements are based on time-of-flight ranging, making them invariant to ambient lighting, which encodes spatial geometry through range measurements without capturing photometric or textural information. This allows the system to operate reliably in varied clinical environments while maintaining patient privacy.
An automated initialization routine is established where the system performs two linear sweeps across the patient’s chest front surface followed by non-rigid template matching to estimate the initial probe position and orientation. This directly addresses operator workload by automating the preparatory steps required before teleoperation begins.
Orientation estimation is refined using a technique that derives the local surface normal via principal component analysis (PCA) applied to the k = 30 nearest neighbors of pS, yielding the eigenvector associated with the smallest eigenvalue as the local surface normal nS. This provides an anatomically neutral and clinically ideal starting orientation for subsequent teleoperated adjustments.