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2024 Optical 3D Metrology, O3DM 2024

作者： Liebender, Christoph Bleier, Michael Nüchter, Andreas Computer Science XVII - Robotics Julius-Maximilians-Universität Würzburg Germany

In this paper, we present an implementation and publish an open source library for binary correspondence search (BICOS), an efficient method for accurate 3D reconstruction from structured light stereo imagery. Starting with two stacks of stereo-rectified images of a scene illuminated by a statistical light pattern the proposed method solves the problem of a pixelwise correspondence search. Our GPU-accelerated implementation reduces the latency of disparity computation using 7MP images on modern hardware down to 20 milliseconds. Based on the algorithm introduced by Dietrich et al. (2019), we extend their approach by increasing the descriptor size while augmenting postprocessing to increase its applicability on other types of pattern projections. Lastly, we provide benchmarks and example reconstructions using a stereo camera setup combined with an off-the-shelf projector to validate the algorithm’s performance. While many state-of-the-art single-shot stereo implementations are included in common computer vision libraries, high performance multi-shot methods are not broadly available. By publishing this method as a freely available library, in both a CUDA and CPU implementation, we hope for others to quickly gain traction in this field. The source code with build instructions and command-line tooling is available at https://***/JMUWrobotics/libBICOS under the GNU LGPLv3. © Author(s) 2024.

关键词： Pixels

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A handheld stereo vision and LiDAR system for outdoor dense RGB-D mapping using depth map completion based on learned priors 8

A handheld stereo vision and LiDAR system for outdoor dense ...

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8th International ISPRS Workshop LowCost 3D - Sensors, Algorithms, Applications

作者： Bleier, Michael Yuan, Yijun Nüchter, Andreas Computer Science XVII: Robotics Julius-Maximilians-Universität Würzburg Germany

This paper proposes a handheld mapping system consisting of a stereo camera setup combined with low-cost automotive LiDAR. The prototype system is applicable to various types of mobile monocular, stereo vision and LiDAR data collection and processing. Capturing dense RGB-D data outdoors with low-cost sensors is challenging, especially when low latency is required. Readily available commercial RGB-D sensors are typically limited to a range of less than 10 m, which is too small to capture large outdoor structures. Currently available low-cost automotive LiDAR scanners feature a suitable range but provide only sparse data. To enable low-latency dense RGB-D scans we augment the sparse LiDAR data with the RGB data stream based on learned models. We apply monocular depth estimation based on a single image and apply scale correction based on learned priors and sparse automotive LiDAR scans. Using the laser scan data, accurate metric information is incorporated directly into the scale estimation stage. For validation, the learning-based depth map completion is compared to traditional LiDAR mapping using scan matching on an outdoor data set acquired with the proposed handheld. While the model-based regression of the sparse LiDAR data produces significantly less accurate results in our experiments, it is able to compute dense RGB-D data from a single sparse 3D scan and monocular RGB image with low latency. Copyright: © 2024 Raniero Beber et al.

关键词： Mapping

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Design and evaluation of an UI for astronauts to control mobile robots on planetary surfaces

Design and evaluation of an UI for astronauts to control mob...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Julius Arzberger Jasper Zevering Fabian Arzberger Andreas Nüchter Computer Science XVII – Robotics Julius-Maximilians-University Würzburg Germany

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

With an advancing plan to send humans on planetary missions again, there will be an increasing need for direct on- surface robot control as interaction between robots and humans will increase with increasing sophistication and complexity of tasks. Remote control from Earth with long delays in communication will not be possible through the direct interaction on the surface. Although a higher degree of autonomy of the robots themselves will also play a role, there is a fundamental lack of interfaces for astronauts to control robots on future planetary missions. This research emerged from participation in the AMADEE-24 Mars simulation by the Austrian Space Forum, which focus on researching future human-robot missions on Mars. We designed and evaluated user interface (UI) prototypes for mobile robots operated by astronauts by using a usercentered design approach and incorporated astronaut feedback. We tested the resulting functional prototype in hypothetical scenarios which demonstrated in a low error rate. This affirms the effectiveness of customized user interfaces for specific mission needs. We advocate for adaptability and user involvement to enhance human-robot interaction for planetary exploration. This work not only addresses the need for intuitive robot control systems but also presents potential UI layouts for future human-robotic mission interfaces.

关键词： Mars Error analysis Space missions Robot control Layout Prototypes Human-robot interaction

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Sensor Cube – A Tool for Hands-on Learning of Sensor Data Processing

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IFAC-PapersOnLine 2024年第16期58卷 23-28页

作者： Michael Bleier Computer Science XVII: Robotics Julius-Maximilians-Universität Würzburg (JMU) D-97074 Würzburg Germany

This paper describes the Sensor Cube, an open-source hardware and software system for hands-on learning of sensor data processing. Various sensors, such as camera, GNSS, and inertial sensors, are included to teach sensor data analysis and processing. The sensor system is designed to work with popular operating systems without special drivers, and the data format is human-readable. Therefore, all sensor data is easily accessible through Python or C++ code without the need to learn an additional framework. In addition, the hardware design, firmware, and code examples for working with the sensor data are available as open-source.

关键词： Sensor systems Sensor data processing Education Teaching hardware Lab experiments

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Inertial LiDAR Motion Distortion Correction on Spherical Mobile Mapping Systems for Planetary Exploration

Inertial LiDAR Motion Distortion Correction on Spherical Mob...

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Space robotics (iSpaRo), International Conference on

作者： Paul Heinisch Fabian Arzberger Andreas Nüchter Computer Science XVII – Robotics Julius-Maximilians-Universität Würzburg Germany

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

In this paper, we present an algorithm countering the motion distortion observed in LiDAR point clouds due to the inherent inertial rotations when recorded with a spherical mobile mapping system. State-of-the-art motion distortion correction algorithms focus on more rotationally restricted systems, such as UAV, rovers, or cars, where often a "dead-reckoning" approach is utilized to account for the vehicles linear velocity. Thus, the algorithm presented in this paper is based on a motion model for spherical systems, taking only the angular velocities into account. We show, despite explicitly omitting a model for the linear velocity of the scanner, and assuming constant angular velocity between two frames, that our approach outperforms state-of-the-art algorithms which use a false model for the linear velocities. To verify this, we test our algorithm on 3D point clouds recorded with a Livox MID-100 mounted on a spherical mobile mapping system, and compare the accuracy to an algorithm proposed in the Livox-SDK. To do this, we have ground truth point clouds available from a terrestrial laser scanner (TLS). Our algorithm consistently reduces the motion distortion from low (5 Hz) to high (50 Hz) LiDAR sampling frequencies, such that the point clouds are well-suited for further processing steps such as odometry or mapping. The code for our algorithm is open source [1].

关键词： Point cloud compression Temperature measurement Temperature sensors Laser radar Three-dimensional displays Robot sensing systems Distortion

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On the 3D trochoidal motion model of LiDAR sensors placed off-centered inside spherical mobile mapping systems

On the 3D trochoidal motion model of LiDAR sensors placed of...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fabian Arzberger Andreas Nüchter Computer Science XVII – Robotics Julius-Maximilians-Universität Würzburg Am Hubland Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We study the motion model of a sensor rigidly mounted inside a ball. Due to the rigid placement inside the ball, the geometry of the sensor trajectory resembles a 3D curate trochoid. A new calibration method for spherical systems estimates the extrinsic parameters of the sensor with respect to the balls center of rotation. We deploy the calibration and motion model on our spherical mobile mapping platform to estimate the trajectory of a LiDAR sensor and compare it to trajectories of state-of-the-art LiDAR-Inertial odometry (LIO) methods. The motion model, which is solely based on IMU measurements, produces comparable results to the LIO methods, sometimes even outperforming them in positional accuracy. Although the LIO methods provide better rotational accuracy due to the utilization of LiDAR data, they struggle to reproduce the trochoidal nature of the trajectory and only provide pose estimations at the LiDAR frequency, whereas the motion model produces a more consistent trochoidal trajectory at the much higher IMU frequency. The results demonstrate the difficulty that current LIO methods have on spherical systems and indicate that our motion model is suitable for overcoming these issues.

关键词： Point cloud compression Geometry Laser radar Image color analysis Predictive models Three-dimensional printing Frequency estimation Data models Trajectory Calibration

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Covariance Based Terrain Mapping for Autonomous Mobile Robots

Covariance Based Terrain Mapping for Autonomous Mobile Robot...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Lennart Werner Pedro Proença Andreas Nüchter Roland Brockers Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Computer Science XVII – Robotics Julius-Maximilians-Universität Würzburg Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present a local, robot-centric navigation map optimized for autonomous mobile robots operating in unknown environments, enhancing their onboard perception systems for collision-free operation with far look-ahead distances. Utilizing a novel converging covariance cell representation, our approach effectively analyzes hazards such as obstacles and hazardous slopes in both terrestrial and aerial navigation contexts. The new technique specifically targets mapping from stereo scenarios with ultra short baseline and highly oblique viewpoints close to the *** methodology surpasses traditional window-based hazard analysis by resolving sub-cell size obstacles and terrain gradients at the individual cell level, thereby avoiding the computational overhead typically associated with such analyses. It leverages a multi-resolution strategy adaptive to the range errors common in stereo vision systems, making it particularly suitable for embedded systems with computational *** includes constant-time queries for height, obstacle presence, and slope details, boasting improvements in run time, memory usage, precision, and resolvable obstacle size compared to existing grid-based mapping algorithms. We validate our approach through rigorous simulation and real-world testing. This technique will be used for the local mapping and collision avoidance on NASA’s CADRE lunar rovers.

关键词： Uncertainty Three-dimensional displays Navigation Moon Terrain mapping Hazards Mobile robots

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Delta Filter - Robust Visual-Inertial Pose Estimation in Real-Time: A Multi-Trajectory Filter on a Spherical Mobile Mapping System

Delta Filter - Robust Visual-Inertial Pose Estimation in Rea...

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European Conference on Mobile Robots (ECMR)

作者： Fabian Arzberger Fabian Wiecha Jasper Zevering Julian Rothe Dorit Borrmann Sergio Montenegro Andreas Nüchter Computer Science XVII - Robotics Julius-Maximilians-Universität Würzburg Am Hubland Germany Computer Science VIII - Aerospace Information Technology Julius-Maximilians-Universität Würzburg Am Hubland Germany THWS Robotics Technische-Hochschule-Würzburg-Schweinfurt Schweinfurt Germany

Many state-of-the-art mobile mapping systems accomplish reliable and robust pose estimation utilizing combinations of inertial measurement units (IMUs), global navigation satellite systems (GNSS), visual-inertial- or LiDAR-inertial odometry (VIO/LIO). However, on a spherical mobile mapping system the underlying inherent rolling motion introduces high angular velocities, thus the quality of pose estimates, images, and laser-scans, degrade. In this work we propose a pose filter design that is able to do real-time sensor fusion between two unreliable trajectories into one, more reliable trajectory. It is a simple yet effective filter design that does not require the user to estimate the uncertainty of the sensors. The approach is not limited to spherical robots and theoretically is also suitable for sensor fusion of an arbitrary number of estimators. This work compares this filter against two pose estimation methods on our spherical system: (1) An approach that is based solely on IMU measurements, and (2) stereo-VIO with an Intel® RealSense™ tracking camera. The proposed “Delta” filter takes as input (1), (2), and a motion model. Our implementation gets rid of the drift in (1) and (2), estimates the scale of the trajectory, and deals with slow and fast motion as well as driving curves. Our source code can be found on github [1].

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