The accurate and robust calibration result of sensors is considered as an important building block to the follow-up research in the autonomous driving and robotics domain. The current works involving extrinsic calibration between 3D LiDARs and monocular cameras mainly focus on target-based and target-less methods. The target-based methods are often utilized offline because of restrictions, such as additional target design and target placement limits. The current target-less methods suffer from feature indeterminacy and feature mismatching in various environments. To alleviate these limitations, we propose a novel target-less calibration approach which is based on the 2D-3D edge point extraction using the occlusion relationship in 3D space. Based on the extracted 2D-3D point pairs, we further propose an occlusion-guided point-matching method that improves the calibration accuracy and reduces computation costs. To validate the effectiveness of our approach, we evaluate the method performance qualitatively and quantitatively on real images from the KITTI dataset. The results demonstrate that our method outperforms the existing target-less methods and achieves low error and high robustness that can contribute to the practical applications relying on high-quality Camera-LiDAR calibration.
Bézier Degradation Modeling for LiDAR-based Human Motion Capture
PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院精密仪器实验室，南京理工大学）；PCA Lab, School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院精密仪器实验室，南京大学）
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatiC multi-LiDAR CalibratiOn and Refinement method in rOad …
Hong Kong University of Science and Technology（香港科技大学）
LiDAR and cameras are two complementary sensors for 3D perception in autonomous driving. LiDAR point clouds have accurate spatial and geometry information, while RGB images provide textural and color data for context reasoning. To exploit LiDAR and cameras jointly, existing fusion methods tend to align each 3D point to only one projected image pixel based on calibration, namely one-to-one mapping. However, the performance of these approaches highly relies on the calibration quality, which is sensitive to the temporal and spatial synchronization of sensors. Therefore, we propose a Dynamic Cross…
UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
A Target-based Multi-LiDAR Multi-Camera Extrinsic Calibration System
Research \& Development, Toyota Material Handling Manufacturing, Bologna Italy Department of Information Engineering, University of Pisa, Pisa, Italy School of Engineering；Materials Science, Queen Mary University of London, London, United Kingdom
Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent studies have demonstrated that LiDAR-based perception is vulnerable to spoofing attacks, in which adversaries spoof a fake vehicle in front of a victim self-driving car by strategically transmitting laser signals to the victim's LiDAR sensor. However, existing attacks suffer from effectiveness and generality limitations. In this work, we perform the first study to explore the general vulnerability of current LiDAR-bas…
CalibRefine: Deep Learning-Based Online Automatic Targetless LiDAR-Camera Calibration with Iterative and Attention-Driven Post-Refinement
Department of Electrical and Computer Engineering, The University of Arizona（电气与计算机工程系，亚利桑那大学）；Center For Urban Informatics and Progress (CUIP), UTC Research Institute, University of Tennessee at Chattanooga（城市信息与进步中心（CUIP）、UTC研究机构、田纳西大学查塔努加分校）；Electrical Engineering Department, University of San Francisco（电气工程系，旧金山大学）
DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network
Department of Control Science & Engineering, the College of Electronics & Information Engineering, Tongji University（控制科学与工程系，电子与信息工程学院，同济大学）；Department of Mechanical Engineering, the School of Mechanical Engineering, Tongji University（机械工程系，机械工程学院，同济大学）；School of Robotics, Hunan University（机器人学院，湖南大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科学技术大学）；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, and Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（控制科学与工程系，电子与信息工程学院，上海智能自主系统研究院，智能自主系统国家重点实验室，智能自主系统前沿科学中心，同济大学）；National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（人机混合增强智能国家重点实验室，人工智能与机器人研究院，西安交通大学）
Oxford Robotics Institute (ORI)（牛津机器人研究所）；University of Oxford（牛津大学）
Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traffic signs and road markings). These difficulties arise from occlusions by other traffic participants as well as changing lighting and/or weather conditions. Moreover, road boundaries have various shapes, colours and structures while motion planning algorithms require accurate and …
College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）；Unmanned Systems Research Center, National Innovation Institute of Defense Technology（国防科技创新研究院无人系统研究中心）
LIDAR is one of the most important sensors for Unmanned Ground Vehicles (UGV). Object detection and classification based on lidar point cloud is a key technology for UGV. In object detection and classification, the mutual occlusion between neighboring objects is an important factor affecting the accuracy. In this paper, we consider occlusion as an intrinsic property of the point cloud data. We propose a novel approach that explicitly model the occlusion. The occlusion property is then taken into account in the subsequent classification step. We perform experiments on the KITTI dataset. Experim…
Graz University of Technology（格拉茨工业大学）；Technical University of Munich（慕尼黑工业大学）；University of Veterinary Medicine, Vienna（维也纳兽医大学）
While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we propose a method to generate attribution maps for the detected objects in order to better understand the behavior of such models. These maps indicate the importance of each 3D point in predicting the specific objects. Our method works with black-box models: We do not require any prior knowledge of the architecture nor access to the model's internals, like parameters, activations or gradients. Our efficient perturbation…
Tsinghua University（清华大学）；Mogo Auto Intelligence and Telemetics Information Technology Co. Ltd.（蘑菇车联智能网联科技有限公司）
3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework …
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
Dept. of Electronic and Computer Engineering and the Data Driven Computer Engineering Research Centre, University of Limerick（电子与计算机工程系和数据驱动计算机工程研究中心，利默里克大学）；Valeo Vision Systems（瓦莱欧视觉系统）；Queen Mary University of London（伦敦女王大学）
This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling. The calibration parameters are estimated by optimizing an objective function that penalizes distances between 2D and re-projected 3D motion vectors obtained from time-synchronized image and point cloud sequences. For upsampling, a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving the LiDAR measurements is propose…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
In this paper, we present TEScalib, a novel extrinsic self-calibration approach of LiDAR and stereo camera using the geometric and photometric information of surrounding environments without any calibration targets for automated driving vehicles. Since LiDAR and stereo camera are widely used for sensor data fusion on automated driving vehicles, their extrinsic calibration is highly important. However, most of the LiDAR and stereo camera calibration approaches are mainly target-based and therefore time consuming. Even the newly developed targetless approaches in last years are either inaccurate…
In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors. Compared to the previous CNN-based methods that concatenate the feature maps of the RGB image and decalibrated depth image, we exploit the cost volume inspired by the PWC-Net for feature matching. Besides the smooth L1-Loss of the predicted extrinsic calibration parameters, an additional point cloud loss is applied. Instead of regress the extrinsic parameters between LiDAR and camera directly, we predict the decalibrated deviation from initial calibration to the ground…
Texas A&M University（德克萨斯A&M大学）；DEVCOM Army Research Laboratory (ARL)（DEVCOM陆军研究实验室）
We propose an algorithm for automatic, targetless, extrinsic calibration of a LiDAR and camera system using semantic information. We achieve this goal by maximizing mutual information (MI) of semantic information between sensors, leveraging a neural network to estimate semantic mutual information, and matrix exponential for calibration computation. Using kernel-based sampling to sample data from camera measurement based on LiDAR projected points, we formulate the problem as a novel differentiable objective function which supports the use of gradient-based optimization methods. We also introduc…
Mercedes-Benz AG（梅赛德斯-奔驰股份公司）；University of Freiburg（弗莱堡大学）
The calibration of deep learning-based perception models plays a crucial role in their reliability. Our work focuses on a class-wise evaluation of several model's confidence performance for LiDAR-based semantic segmentation with the aim of providing insights into the calibration of underrepresented classes. Those classes often include VRUs and are thus of particular interest for safety reasons. With the help of a metric based on sparsification curves we compare the calibration abilities of three semantic segmentation models with different architectural concepts, each in a in deterministic and …
The Hong Kong University of Science and Technology（香港科技大学）；Unity-Drive technology Inc（Unity-Drive科技公司）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing …
CLRNet: Targetless Extrinsic Calibration for Camera, Lidar and 4D Radar Using Deep Learning
Hungarian Research Network Institute for Computer Science and Control (HUN-REN SZTAKI)（匈牙利研究网络计算机科学与控制研究所）；Pázmány Péter Catholic University（帕兹曼·佩斯特天主教大学）；Intelligent Vehicles Group, TU Delft（智能车辆组，代尔夫特理工大学）；Perciv AI
Targetless LiDAR-Camera Calibration with Neural Gaussian Splatting
Department of Interdisciplinary Program in Artificial Intelligence, Seoul National University（人工智能交叉学科项目系，首尔国立大学）；Department of Artificial Intelligence, Yonsei University（人工智能系，延世大学）
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（大数据与脑计算北京先进创新中心）；Hangzhou Innovation Institution, Beihang University（北京航空航天大学杭州创新研究院）
Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications. However, the non-uniformity of its scanning pattern, and the inconsistency of the ranging error distribution bring challenges to its calibration task. In this paper, we proposed a fully automatic calibration method for the non-repetitive scanning SSL and camera systems. First, a temporal-spatial-based geometric feature refinement method is presented, to extract effective features from SSL p…
V2I-Calib: A Novel Calibration Approach for Collaborative Vehicle and Infrastructure LiDAR Systems
China University of Mining and Technology -Beijing（中国矿业大学（北京））；Tsinghua University（清华大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
Texas A&M University（得克萨斯农工大学）；DEVCOM Army Research Laboratory (ARL)（陆军研究实验室）
This paper proposes SemCal: an automatic, targetless, extrinsic calibration algorithm for a LiDAR and camera system using semantic information. We leverage a neural information estimator to estimate the mutual information (MI) of semantic information extracted from each sensor measurement, facilitating semantic-level data association. By using a matrix exponential formulation of the $se(3)$ transformation and a kernel-based sampling method to sample from camera measurement based on LiDAR projected points, we can formulate the LiDAR-Camera calibration problem as a novel differentiable objective…
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear. The method based on statistical optimization has the disadvantage that the requirement of the number of laser scanner's channels is relatively high. Calibration equipments with extreme accuracy for panoramic camera-laser scanner system are costly. Facing all these in the calibration of panoramic camera-laser scanner system, a method based on supervised learning is proposed. Firstly, corresponding feature points of pa…
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly regi…
The Hong Kong University of Science and Technology（香港科技大学）
Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and perception systems. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration without any calibration target, prior environmental information, and initial values of the extrinsic parameters. Our approach starts with a hand-eye calibration for automatic initialization by aligning the estimated motions of each sensor. The resulting para…
This paper proposes a simple self-calibration method for the internal time synchronization of MEMS(Micro-electromechanical systems) LiDAR during research and development. Firstly, we introduced the problem of internal time misalignment in MEMS lidar. Then, a robust Minimum Vertical Gradient(MVG) prior is proposed to calibrate the time difference between the laser and MEMS mirror, which can be calculated automatically without any artificial participation or specially designed cooperation target. Finally, actual experiments on MEMS LiDARs are implemented to demonstrate the effectiveness of the p…
Institute of Industrial Science, The University of Tokyo（东京大学产业技术研究所）；Microsoft（微软）
In this paper, we propose a method of targetless and automatic Camera-LiDAR calibration. Our approach is an extension of hand-eye calibration framework to 2D-3D calibration. By using the sensor fusion odometry method, the scaled camera motions are calculated with high accuracy. In addition to this, we clarify the suitable motion for this calibration method. The proposed method only requires the three-dimensional point cloud and the camera image and does not need other information such as reflectance of LiDAR and to give initial extrinsic parameter. In the experiments, we demonstrate our method…
Iterative Camera-LiDAR Extrinsic Optimization via Surrogate Diffusion
School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；Centre for Robotics Research, Department of Engineering, King’s College London（机器人研究中心，工程系，伦敦大学国王学院）；School of Integrated Circuits and Electronics, Beijing Institute of Technology（集成电路与电子学院，北京理工大学）
The Institute of Industrial Science, The University of Tokyo（东京大学生产技术研究所）；The National Institute of Advanced Industrial Science and Technology (AIST)（日本产业技术综合研究所）
