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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

Recalibrating the KITTI Dataset camera setup for improved odometry accuracy

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arXiv 2021年

作者： Cvišić, Igor Marković, Ivan Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to benchmark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the proposed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods. © 2021, CC BY.

关键词： Stereo image processing

Designing a Haptic Interface for Enhanced Non-Verbal Human-Robot Interaction: Integrating Heart and Lung Emotional Feedback

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Designing a Haptic Interface for Enhanced Non-Verbal Human-R...

IEEE-RAS International Conference on Humanoid Robots

作者： Adnan Saood Yang Liu Heng Zhang Adriana Tapus U2IS ENSTA Paris Institut Polytechnique de Paris Paris France LTCI Laboratory Telecom Paris Institut Polytechnique de Paris Paris France Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institut Polytechnique de Paris France

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

Using non-verbal cues such as breathing and heartbeat signals as media for Human-Robot Interaction (HRI) enables subtle exchanges between humans and robots beyond explicit verbal or visual cues. This study describes the development of a novel pneumatic haptic interface that replicates human breathing and heartbeat animations for a humanoid robot and investigates its application for enhancing non-verbal HRI. The experimental results validate the effectiveness of this haptic device and highlight the significance of non-verbal cues in conveying emotional states. Introducing the haptic interface as a means for delivering involuntary, non-verbal emotional signals significantly enhances human-robot interaction compared to using gestures alone.

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Recalibrating the KITTI Dataset Camera Setup for Improved Odometry Accuracy

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Recalibrating the KITTI Dataset Camera Setup for Improved Od...

European Conference on Mobile Robots (ECMR)

作者： Igor Cvišić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics University of Zagreb Faculty of Electrical Engineering and Computing Croatia

Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

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Dynamics Modeling using Visual Terrain Features for High-Speed autonomous Off-Road Driving

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arXiv 2024年

作者： Gibson, Jason Alavilli, Anoushka Tevere, Erica Theodorou, Evangelos A. Spieler, Patrick Autonomous Control and Decision Systems Lab Georgia Institute of Technology AtlantaGA30332 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States NASA Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States

Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as traction, tire slip, and rolling resistance. To achieve effective planning in such environments, it is crucial to have a dynamics model that can accurately anticipate these conditions. In this work, we present a hybrid model that predicts the changing dynamics induced by the terrain as a function of visual inputs. We leverage a pre-trained visual foundation model (VFM) DINOv2, which provides rich features that encode fine-grained semantic information. To use this dynamics model for planning, we propose an end-to-end training architecture for a projection distance independent feature encoder that compresses the information from the VFM, enabling the creation of a lightweight map of the environment at runtime. We validate our architecture on an extensive dataset (hundreds of kilometers of aggressive off-road driving) collected across multiple locations as part of the DARPA Robotic Autonomy in Complex Environments with Resiliency (RACER) program. https://***/watch?v=dycTXxEosMk © 2024, CC BY.

关键词： Off road vehicles

Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation

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Numerical study on the flow field for deep-sea hydrothermal ...

17th Asian Congress of Fluid Mechanics (ACFM 2023)

作者： X. -H. Xu J. -C. Liu H. -R. Hou Q. -H. Meng Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin 300072 People's Republic of China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin 300072 People's Republic of China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k- ε turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles.

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A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems

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arXiv 2022年

作者： Prudencio, Rafael Figueiredo Maximo, Marcos R.O.A. Colombini, Esther Luna The Laboratory of Robotics and Cognitive Systems Computing Institute University of Campinas Campinas13083-852 Brazil The Autonomous Computational Systems Laboratory Computer Science Division Aeronautics Institute of Technology São José dos Campos12228-900 Brazil

With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field. © 2022, CC BY.

关键词： Reinforcement learning

Vehicle Perception from a Single Image for autonomous Driving Using Deformable Model Representation and Deep Learning

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SSRN

SSRN 2022年

作者： Lu, Feixiang Liu, Zongdai Song, Xibin Zhou, Dingfu Li, Wei Miao, Hui Liao, Miao Zhang, Liangjun Zhou, Bin Yang, Ruigang Manocha, Dinesh State Key Laboratory of Virtual Reality Technology and Systems Beihang University China Robotics and Autonomous Driving Laboratory Baidu Research China University of Maryland College Park United States Peng Cheng Laboratory Shenzhen China

We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving. Our approach combines the strengths of deep learning and the elegance of traditional techniques from part-based deformable model representation to produce high-quality 3D models in the presence of severe occlusions. We present a new part-based deformable vehicle model that is used for instance segmentation and automatically generate a dataset that contains dense correspondences between 2D images and 3D models. We also present a novel end-to-end deep neural network to predict dense 2D/3D mapping and highlight its benefits. Based on the dense mapping, we are able to compute precise 6-DoF poses and 3D reconstruction results at almost interactive rates on a commodity GPU. We have integrated these algorithms with an autonomous driving system. In practice, our method outperforms the state-of-the-art methods for all major vehicle parsing tasks: 2D instance segmentation by 4.4 points (mAP), 6-DoF pose estimation by 9.11 points, and 3D detection by 1.37. Moreover, we will release all of the source code, dataset, and the trained model on Github. © 2022, The Authors. All rights reserved.

关键词： autonomous vehicles

Mathematical modeling of stable position of manipulator mounted on unmanned aerial vehicle 15th

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Mathematical modeling of stable position of manipulator moun...

15th International Conference on Electromechanics and robotics Zavalishin’s Readings, ERZR 2020

作者： Nguyen, Vinh Vu, Quyen Ronzhin, Andrey Department of Electromechanics and Robotics SUAI Bolshaya Morskaya Str. 67 Saint-Petersburg190000 Russia Laboratory of Autonomous Robotic Systems SPIIRAS 14-th Line of V.I. 39 Saint-Petersburg199178 Russia

ISBN: (纸本)9789811555794

The stability of the manipulator is important issue especially in the domain of unmanned aerial vehicle (UAV). Any movements of manipulator greatly affects UAV stability. In particular, the horizontal shift of the center of gravity requires of the UAV the powerful controller, that change forced the propellers of quadrotor to bring the UAV to a stable state. In this paper, we have studied the movements of manipulator and its center of gravity at least horizontally when UAV is in hover mode. As a result, we developed mathematical model and software for calculation of deflection angles between successive links of the manipulator, which provides deviation of the center of mass (COM) of the UAV horizontally less than 1 mm. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

关键词： Manipulators