Real-Time Point Cloud Fusion of Multi-LiDAR Infrastructure Sensor Setups with Unknown Spatial Location and Orientation
RWTH Aachen University（亚琛工业大学）
Laurent Kloeker, Christian Kotulla, Lutz Eckstein
备注：
Accepted to be published as part of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, September 20-23, 2020
The use of infrastructure sensor technology for traffic detection has already been proven several times. However, extrinsic sensor calibration is still a challenge for the operator. While previous approaches are unable to calibrate the sensors without the use of reference objects in the sensor field of view (FOV), we present an algorithm that is completely detached from external assistance and runs fully automatically. Our method focuses on the high-precision fusion of LiDAR point clouds and is evaluated in simulation as well as on real measurements. We set the LiDARs in a continuous pendulum motion in order to simulate real-world operation as closely as possible and to increase the demands on the algorithm. However, it does not receive any information about the initial spatial location and orientation of the LiDARs throughout the entire measurement period. Experiments in simulation as well as with real measurements have shown that our algorithm performs a continuous point cloud registration of up to four 64-layer LiDARs in real-time. The averaged resulting translational error is within a few centimeters and the averaged error in rotation is below 0.15 degrees.
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Shanghai AI Laboratory（上海人工智能实验室）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
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 …
University of Coimbra（科英布拉大学）；Institute of Systems and Robotics（系统与机器人研究所）
LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications. With the success of DL approaches in learning useful information from 3D LiDARs, place recognition has also benefited from this modality, which has led to higher re-localization and loop-closure detection performance, particularly, in environments with significant changing conditions. Despite the progress in this field, the extraction of proper and efficient descriptors from 3D LiDAR data that are invariant to changing conditions and orientation is still …
University of Waterloo（滑铁卢大学）；Central University of Finance and Economics（中央财经大学）
Stack interchanges are essential components of transportation systems. Mobile laser scanning (MLS) systems have been widely used in road infrastructure mapping, but accurate mapping of complicated multi-layer stack interchanges are still challenging. This study examined the point clouds collected by a new Unmanned Aerial Vehicle (UAV) Light Detection and Ranging (LiDAR) system to perform the semantic segmentation task of a stack interchange. An end-to-end supervised 3D deep learning framework was proposed to classify the point clouds. The proposed method has proven to capture 3D features in co…
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；North Carolina State University（北卡罗来纳州立大学）；Routescene Inc.（Routescene公司）
In airborne light detection and ranging (LiDAR) systems, misalignments between the LiDAR-scanner and the inertial navigation system (INS) mounted on an unmanned aerial vehicle (UAV)'s frame can lead to inaccurate 3D point clouds. Determining the orientation offset, or boresight error is key to many LiDAR-based applications. In this work, we introduce a mixed-integer quadratically constrained quadratic program (MIQCQP) that can globally solve this misalignment problem. We also propose a nested spatial branch and bound (nsBB) algorithm that improves computational performance. The nsBB relies on …
Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation
Engineering Research Institute of Elche (I3E)（埃尔切工程研究所 (I3E)）；Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI)（瓦伦西亚人工智能研究生院与研究网络 (ValgrAI)）
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）
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences（中国科学院遥感与数字地球研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）
Point cloud classification plays an important role in a wide range of airborne light detection and ranging (LiDAR) applications, such as topographic mapping, forest monitoring, power line detection, and road detection. However, due to the sensor noise, high redundancy, incompleteness, and complexity of airborne LiDAR systems, point cloud classification is challenging. In this paper, we proposed a directionally constrained fully convolutional neural network (D-FCN) that can take the original 3D coordinates and LiDAR intensity as input; thus, it can directly apply to unstructured 3D point clouds…
Lidar data can be used to generate point clouds for the navigation of autonomous vehicles or mobile robotics platforms. Scan matching, the process of estimating the rigid transformation that best aligns two point clouds, is the basis for lidar odometry, a form of dead reckoning. Lidar odometry is particularly useful when absolute sensors, like GPS, are not available. Here we propose the Iterative Closest Ellipsoidal Transform (ICET), a scan matching algorithm which provides two novel improvements over the current state-of-the-art Normal Distributions Transform (NDT). Like NDT, ICET decomposes …
Co-Registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quilt challenging because the different imaging mechanism causes significant geometric and radiometric distortions between such data. To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation. In the proposed method, the LiDAR point cloud data is first transformed into the intensity map, which is used as the reference image. Then, we employ the Fast operator to extract uniformly distributed interest points in the aerial imag…
International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）
LiDAR point clouds provide rich geometric information, which is particularly useful for the analysis of complex scenes of urban regions. Finding structural and semantic differences between two different three-dimensional point clouds, say, of the same region but acquired at different time instances is an important problem. A comparison of point clouds involves computationally expensive registration and segmentation. We are interested in capturing the relative differences in the geometric uncertainty and semantic content of the point cloud without the registration process. Hence, we propose an …
Evaluation of Flight Parameters in UAV-based 3D Reconstruction for Rooftop Infrastructure Assessment
Ingenuity Labs Research Institute, Queen's University, Canada（Ingenuity Labs研究机构、女王大学，加拿大）；Department of Civil Engineering, Queen's University, Canada（土木工程系、女王大学，加拿大）；Department of Electrical and Computer Engineering, Queen's University, Canada（电气与计算机工程系、女王大学，加拿大）
Most existing perception systems rely on sensory data acquired from cameras, which perform poorly in low light and adverse weather conditions. To resolve this limitation, we have witnessed advanced LiDAR sensors become popular in perception tasks in autonomous driving applications. Nevertheless, their usage in traffic monitoring systems is less ubiquitous. We identify two significant obstacles in cost-effectively and efficiently developing such a LiDAR-based traffic monitoring system: (i) public LiDAR datasets are insufficient for supporting perception tasks in infrastructure systems, and (ii)…
Large driving datasets are a key component in the current development and safeguarding of automated driving functions. Various methods can be used to collect such driving data records. In addition to the use of sensor equipped research vehicles or unmanned aerial vehicles (UAVs), the use of infrastructure sensor technology offers another alternative. To minimize object occlusion during data collection, it is crucial to record the traffic situation from several perspectives in parallel. A fusion of all raw sensor data might create better conditions for multi-object detection and tracking (MODT)…
GRAR: Glass-induced Reflection Artifact Removal in LiDAR Point Clouds
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；School of Design, Hunan University（湖南大学设计学院）；School of Finance and Statistics, Hunan University（湖南大学金融与统计学院）
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（自动化与智能感知学院，上海交通大学）；Global College, Shanghai Jiao Tong University（全球学院，上海交通大学）
LiDAR-based roadside perception is a cornerstone of advanced Intelligent Transportation Systems (ITS). While considerable research has addressed optimal LiDAR placement for infrastructure, the profound impact of differing LiDAR scanning patterns on perceptual performance remains comparatively under-investigated. The inherent nature of various scanning modes - such as traditional repetitive (mechanical/solid-state) versus emerging non-repetitive (e.g. prism-based) systems - leads to distinct point cloud distributions at varying distances, critically dictating the efficacy of object detection an…
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）
PointSSC: A Cooperative Vehicle-Infrastructure Point Cloud Benchmark for Semantic Scene Completion
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；Mogo Auto Intelligence and Telematics Information Technology Company Ltd.（摩智智行智能与远程信息技术有限公司）
Western University（韦仕敦大学）；Institute for Earth and Space Exploration（地球与空间探索研究所）
In geology, a key activity is the characterisation of geological structures (surface formation topology and rock units) using Planar Orientation measurements such as Strike, Dip and Dip Direction. In general these measurements are collected manually using basic equipment; usually a compass/clinometer and a backboard, recorded on a map by hand. Various computing techniques and technologies, such as Lidar, have been utilised in order to automate this process and update the collection paradigm for these types of measurements. Techniques such as Structure from Motion (SfM) reconstruct of scenes an…
HiSC4D: Human-centered interaction and 4D Scene Capture in Large-scale Space Using Wearable IMUs and LiDAR
Xiamen University（厦门大学）；School of Informatics, Xiamen University（厦门大学信息学院）；Fujian Key Laboratory of Sensing and Computing for Smart Cities（福建省智慧城市感知与计算重点实验室）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）
As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of sin…
LiDAR-based End-to-end Temporal Perception for Vehicle-Infrastructure Cooperation
University of Science and Technology Beijing（北京科技大学）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）；The University of Hong Kong（香港大学）；Chengfang Financial Information Technology Services CO.LTD（诚方金融信息技术服务有限公司）
State Key Laboratory of Industrial Control Technology（工业控制技术国家重点实验室）；Institute of Cyber-Systems and Control, Zhejiang University（浙江大学网络空间安全与控制研究所）
Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the appearance change. In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation. The orientation can further be used as the initial value for the down-stream local optimal …
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs. We design a 3D object detection model that can detect traffic participants in roadside LiDARs in real-time. Our model uses an existing 3D detector as a baseline and improves its accuracy. To prove the effectiveness of our proposed modules, we train and evaluate the model on three different vehicle and infrastructure datasets. To show the domain adaptation ability of our detector, we train it on an infrastructure dataset from China and perform transfer learni…
Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study
University of Applied Sciences Esslingen（埃斯林根应用科学大学）；Faculty of Computer Science and Engineering（计算机科学与工程学院）；Institute for Intelligent Systems（智能系统研究所）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDA…
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e.g. camera, LIDAR) typically increases the robustness of 3D detectors. However, the efficient and effective fusion of different features captured from LIDAR and camera is still challenging, especially due to the sparsity and irregularity of point cloud distributions. This notwithstanding, point clouds offer useful complementary information. In this paper, we would like to leverage the advantages of LIDAR and camera sensors by proposing a deep neural network architecture for the …
Monocular camera sensors are vital to intelligent vehicle operation and automated driving assistance and are also heavily employed in traffic control infrastructure. Calibrating the monocular camera, though, is time-consuming and often requires significant manual intervention. In this work, we present an extrinsic camera calibration approach that automatizes the parameter estimation by utilizing semantic segmentation information from images and point clouds. Our approach relies on a coarse initial measurement of the camera pose and builds on lidar sensors mounted on a vehicle with high-precisi…
