Over the last decade, robotic perception algorithms have significantly benefited from the rapid advances in deep learning (DL). Indeed, a significant amount of the autonomy stack of different commercial and research platforms relies on DL for situational awareness, especially vision sensors. This work explores the potential of general-purpose DL perception algorithms, specifically detection and segmentation neural networks, for processing image-like outputs of advanced lidar sensors. Rather than processing the three-dimensional point cloud data, this is, to the best of our knowledge, the first work to focus on low-resolution images with 360\textdegree field of view obtained with lidar sensors by encoding either depth, reflectivity, or near-infrared light in the image pixels. We show that with adequate preprocessing, general-purpose DL models can process these images, opening the door to their usage in environmental conditions where vision sensors present inherent limitations. We provide both a qualitative and quantitative analysis of the performance of a variety of neural network architectures. We believe that using DL models built for visual cameras offers significant advantages due to the much wider availability and maturity compared to point cloud-based perception.
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
As the autonomous driving industry is slowly maturing, visual map localization is quickly becoming the standard approach to localize cars as accurately as possible. Owing to the rich data returned by visual sensors such as cameras or LiDARs, researchers are able to build different types of maps with various levels of details, and use them to achieve high levels of vehicle localization accuracy and stability in urban environments. Contrary to the popular SLAM approaches, visual map localization relies on pre-built maps, and is focused solely on improving the localization accuracy by avoiding er…
Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an essential topic for 3D object detection in autonomous driving. Existing methods mostly adopt an independent dual-branch framework to generate LiDAR and camera BEV, then perform an adaptive modality fusion. Since point clouds provide more accurate localization and geometry information, they could serve as a reliable spatial prior to acquiring relevant semantic information from the images. Therefore, we design a LiDAR-Guided View Transformer (LGVT) to effectively obtain the camera representation in BEV space and th…
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学研究所）
The core task of any autonomous driving system is to transform sensory inputs into driving commands. In end-to-end driving, this is achieved via a neural network, with one or multiple cameras as the most commonly used input and low-level driving command, e.g. steering angle, as output. However, depth-sensing has been shown in simulation to make the end-to-end driving task easier. On a real car, combining depth and visual information can be challenging, due to the difficulty of obtaining good spatial and temporal alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can outpu…
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discovered that this underlying assumption makes the current fusion framework infeasible to produce any prediction when there is a LiDAR malfunction, regardless of minor or major. This fundamentally limits the deployment capability to realistic autonomous driving scenarios. In contrast, we propose a surprisingly simple yet novel fusion framework, dubbed BEVFusion,…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Object detection in state-of-the-art Autonomous Vehicles (AV) framework relies heavily on deep neural networks. Typically, these networks perform object detection uniformly on the entire camera LiDAR frames. However, this uniformity jeopardizes the safety of the AV by giving the same priority to all objects in the scenes regardless of their risk of collision to the AV. In this paper, we present a new end-to-end pipeline for AV that introduces the concept of LiDAR cluster first and camera inference later to detect and classify objects. The benefits of our proposed framework are twofold. First, …
Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches to predict depth information from limited sensor input, they are usually a simple concatenation of sparse LIDAR features and dense RGB features through an end-to-end fusion architecture. In this paper, we introduce an inductive late-fusion block which better fuses different sensor modalities inspire…
This paper presents a novel method for pedestrian detection and tracking by fusing camera and LiDAR sensor data. To deal with the challenges associated with the autonomous driving scenarios, an integrated tracking and detection framework is proposed. The detection phase is performed by converting LiDAR streams to computationally tractable depth images, and then, a deep neural network is developed to identify pedestrian candidates both in RGB and depth images. To provide accurate information, the detection phase is further enhanced by fusing multi-modal sensor information using the Kalman filte…
Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera-based solutions have limited range. We seek to increase the maximum range of self-driving vehicles' depth perception modules for the sake of better safety. To that end, we propose a novel three-camera system that utilizes small field of view cameras. Our system, along with our novel algorithm for computing metric depth, does not require full pre-calibration and can output dense depth maps with practically acceptable accuracy for scenes and objects at long distances not well covered by …
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…
CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion
Wuhan University of Technology（武汉理工大学）；University of North Carolina at Charlotte（北卡罗来纳州立大学）；Wuhan University（武汉大学）；Southwest Jiaotong University（西南交通大学）；The Hong Kong Polytechnic University（香港理工大学）
Southern University of Science and Technology（南方科技大学）；Sifakis Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology（南方科技大学西法基斯可信自主系统研究院）；Pazhou Lab（琶洲实验室）
Multi-object tracking (MOT) with camera-LiDAR fusion demands accurate results of object detection, affinity computation and data association in real time. This paper presents an efficient multi-modal MOT framework with online joint detection and tracking schemes and robust data association for autonomous driving applications. The novelty of this work includes: (1) development of an end-to-end deep neural network for joint object detection and correlation using 2D and 3D measurements; (2) development of a robust affinity computation module to compute occlusion-aware appearance and motion affini…
FlatFusion: Delving into Details of Sparse Transformer-based Camera-LiDAR Fusion for Autonomous Driving
School of Computer Science and School of Artificial Intelligence, Shanghai Jiao Tong University（计算机科学学院和人工智能学院，上海交通大学）；Carnegie Mellon University（卡内基梅隆大学）
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
Nanyang Technological University（南洋理工大学）；East China Normal University（华东师范大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；New York University（纽约大学）
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）
Scuola Superiore Sant’Anna（圣安娜高等学校）；Hitachi Rail STS（日立铁路STS公司）
The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, which require the artificial perception of the railway environment through different types of sensors, including cameras, LiDARs, wheel encoders, and inertial measurement units. A promising approach for processing such sensory data is the use of deep learning models, which proved to achieve excellent performance in other application domains, as robotics and self-driving cars. However, testing new algorithms and solutions…
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 homogeneous transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm translation errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors i…
Vellore Institute of Technology, Chennai（金奈韦洛尔理工大学）；Birla Institute of Technology and Science, Hyderabad（海得拉巴比尔拉理工学院）；Veermata Jijabai Technological Institute, Mumbai（孟买韦尔马塔·吉贾拜技术学院）；International Institute of Information and Technology, Hyderabad（海得拉巴国际信息技术学院）
With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds fro…
Southern University of Science and Technology（南方科技大学）；Jimei University（集美大学）
Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-flight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；The University of Tokyo（东京大学）；Line Corporation（Line公司）
Estimating the pose of an unmanned aerial vehicle (UAV) or drone is a challenging task. It is useful for many applications such as navigation, surveillance, tracking objects on the ground, and 3D reconstruction. In this work, we present a LiDAR-camera-based relative pose estimation method between a drone and a ground vehicle, using a LiDAR sensor and a fisheye camera on the vehicle's roof and another fisheye camera mounted under the drone. The LiDAR sensor directly observes the drone and measures its position, and the two cameras estimate the relative orientation using indirect observation of …
Oxford Robotics Institute（牛津机器人研究所）；University of Oxford（牛津大学）
We present a multi-camera LiDAR inertial dataset of 4.5 km walking distance as an expansion of the Newer College Dataset. The global shutter multi-camera device is hardware synchronized with both the IMU and LiDAR, which is more accurate than the original dataset with software synchronization. This dataset also provides six Degrees of Freedom (DoF) ground truth poses at LiDAR frequency (10 Hz). Three data collections are described and an example use case of multi-camera visual-inertial odometry is demonstrated. This expansion dataset contains small and narrow passages, large scale open spaces,…
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…
Australian Centre for Field Robotics (ACFR)（澳大利亚野外机器人中心（ACFR））；University of Sydney（悉尼大学）
To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high-level contextual understanding of the surroundings is necessary to plan and execute accurate driving maneuvers. This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images. The output of a convolutional neural network (CNN) is used as classifier to obtain the labels of the environment. The transference of semantic information between the labelled image and the lidar point cloud is p…
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）；CWB Institute of Autonomous Driving（CWB自动驾驶研究院）
The combination of LiDARs and cameras enables a mobile robot to perceive environments with multi-modal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating us to introduce novel event cameras to make LiDAR-camera fusion more complete and robust. However, to jointly exploit these sensors, the challenging extrinsic calibration problem should be addressed. This paper proposes an automatic checkerboard-based approach to calibrate extrinsics between a LiDAR and a frame/event camera, where four contributio…
Australian Centre for Field Robotics（澳大利亚野外机器人学中心）；University of Sydney（悉尼大学）
This paper proposes an automated method to obtain the extrinsic calibration parameters between a camera and a 3D lidar with as low as 16 beams. We use a checkerboard as a reference to obtain features of interest in both sensor frames. The calibration board centre point and normal vector are automatically extracted from the lidar point cloud by exploiting the geometry of the board. The corresponding features in the camera image are obtained from the camera's extrinsic matrix. We explain the reasons behind selecting these features, and why they are more robust compared to other possibilities. To…
In this paper, we present a multi-camera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surround-view image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans of the virtual LiDAR are generated by discretizing the contours of the free space. As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results. Only those feature…
Domain gaps of sensor modalities pose a challenge for the design of autonomous robots. Taking a step towards closing this gap, we propose two unsupervised training frameworks for finding a common representation of LiDAR and camera data. The first method utilizes a double Siamese training structure to ensure consistency in the results. The second method uses a Canny edge image guiding the networks towards a desired representation. All networks are trained in an unsupervised manner, leaving room for scalability. The results are evaluated using common computer vision applications, and the limitat…
YOCO: You Only Calibrate Once for Accurate Extrinsic Parameter in LiDAR-Camera Systems
China University of Mining and Technology (Beijing)（中国矿业大学（北京））；College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing)（中国矿业大学（北京）地球科学与测绘工程学院）
