In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. Localization is then conducted through an efficient convolutional matching between the embeddings. Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments. Our experiments illustrate the performance of the proposed approach over a large-scale dataset consisting of over 4000km of driving.
The Chinese University of Hong Kong（香港中文大学）；Sensetime（商汤科技）
Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annot…
Ford AV LLC（福特自动驾驶有限责任公司）；Ford Motor Company（福特汽车公司）
This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments. Traditional localization techniques using LIDAR reflectivity rely on high definition reflectivity maps generated from a mapping vehicle. The cost and effort required to maintain such prior maps are generally very high because it requires a fleet of expensive mapping vehicles. In this work we propose a localization technique where the vehicle localizes using aerial/satellite imagery, eradicating the need to develop and maintain complex high-de…
School of Intelligence Science and Technology（智能科学与技术学院）
The ability for a moving agent to localize itself in environment is the basic demand for emerging applications, such as autonomous driving, etc. Many existing methods based on multiple sensors still suffer from drift. We propose a scheme that fuses map prior and vanishing points from images, which can establish an energy term that is only constrained on rotation, called the direction projection error. Then we embed these direction priors into a visual-LiDAR SLAM system that integrates camera and LiDAR measurements in a tightly-coupled way at backend. Specifically, our method generates visual r…
Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar…
The integration of multiple cameras and 3D Li- DARs has become basic configuration of augmented reality devices, robotics, and autonomous vehicles. The calibration of multi-modal sensors is crucial for a system to properly function, but it remains tedious and impractical for mass production. Moreover, most devices require re-calibration after usage for certain period of time. In this paper, we propose a single-shot solution for calibrating extrinsic transformations among multiple cameras and 3D LiDARs. We establish a panoramic infrastructure, in which a camera or LiDAR can be robustly localize…
Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality. It is also a prohibitive factor for those applications to be in mass production, since the state-of-the-art, centimeter-level pose estimation often requires long mapping procedures and expensive localization sensors, e.g. LiDAR and high precision GPS/IMU, etc. To overcome the cost barrier, we propose a neural network based solution to localize a consumer degree RGB camera within a prior sparse LiDAR map with comparable centimeter-level precision. We achi…
Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the surroundings from LiDAR data. However, these models encounter a large domain gap while deploying them on vehicles equipped with different LiDAR setups which drastically decreases their performance. Fine-tuning the model for every new setup is infeasible due to the expensive and cumbersome process of recording and manually labeling new data. Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill t…
Technical University of Munich（慕尼黑工业大学）；Sun Yat-sen University（中山大学）
Localization in the environment is an essential navigational capability for animals and mobile robots. In the indoor environment, the global localization problem remains challenging to be perfectly solved with probabilistic methods. However, animals are able to instinctively localize themselves with much less effort. Therefore, it is intriguing and promising to seek biological inspiration from animals. In this paper, we present a biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach. The experiment …
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…
Robust and accurate, map-based localization is crucial for autonomous mobile systems. In this paper, we exploit range images generated from 3D LiDAR scans to address the problem of localizing mobile robots or autonomous cars in a map of a large-scale outdoor environment represented by a triangular mesh. We use the Poisson surface reconstruction to generate the mesh-based map representation. Based on the range images generated from the current LiDAR scan and the synthetic rendered views from the mesh-based map, we propose a new observation model and integrate it into a Monte Carlo localization …
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…
Localization is a crucial capability for mobile robots and autonomous cars. In this paper, we address learning an observation model for Monte-Carlo localization using 3D LiDAR data. We propose a novel, neural network-based observation model that computes the expected overlap of two 3D LiDAR scans. The model predicts the overlap and yaw angle offset between the current sensor reading and virtual frames generated from a pre-built map. We integrate this observation model into a Monte-Carlo localization framework and tested it on urban datasets collected with a car in different seasons. The experi…
We present a robust and precise localization system that achieves centimeter-level localization accuracy in disparate city scenes. Our system adaptively uses information from complementary sensors such as GNSS, LiDAR, and IMU to achieve high localization accuracy and resilience in challenging scenes, such as urban downtown, highways, and tunnels. Rather than relying only on LiDAR intensity or 3D geometry, we make innovative use of LiDAR intensity and altitude cues to significantly improve localization system accuracy and robustness. Our GNSS RTK module utilizes the help of the multi-sensor fus…
Semantic2D: Enabling Semantic Scene Understanding with 2D Lidar Alone
School of Computing and Data Science, The University of Hong Kong（计算与数据科学学院，香港大学）；Department of Mechanical Engineering, Temple University（机械工程系， Temple大学）
U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequences
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；National University of Singapore（新加坡国立大学）；Fudan University（复旦大学）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）；SKL-TI
Poznan University of Technology（波兹南理工大学）；Warsaw University of Technology（华沙理工大学）
The 3D LiDAR place recognition aims to estimate a coarse localization in a previously seen environment based on a single scan from a rotating 3D LiDAR sensor. The existing solutions to this problem include hand-crafted point cloud descriptors (e.g., ScanContext, M2DP, LiDAR IRIS) and deep learning-based solutions (e.g., PointNetVLAD, PCAN, LPDNet, DAGC, MinkLoc3D), which are often only evaluated on accumulated 2D scans from the Oxford RobotCar dataset. We introduce MinkLoc3D-SI, a sparse convolution-based solution that utilizes spherical coordinates of 3D points and processes the intensity of …
Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs
Centro Universitario de la Defensa, Zaragoza（萨拉戈萨国防大学中心）；Instituto de Investigación en Ingeniería de Aragón（阿拉贡工程研究院）；University of Zaragoza（萨拉戈萨大学）；Investigación en Ingeniería de Aragón（阿拉贡工程研究院）
NTT Human Informatics Laboratories（NTT人类信息学实验室）；NTT Communication Science Laboratories（NTT通信科学实验室）
Intrinsic image decomposition (IID) is the task that decomposes a natural image into albedo and shade. While IID is typically solved through supervised learning methods, it is not ideal due to the difficulty in observing ground truth albedo and shade in general scenes. Conversely, unsupervised learning methods are currently underperforming supervised learning methods since there are no criteria for solving the ill-posed problems. Recently, light detection and ranging (LiDAR) is widely used due to its ability to make highly precise distance measurements. Thus, we have focused on the utilization…
The fiducial marker system for LiDAR is crucial for the robotic application but it is still rare to date. In this paper, an Intensity Image-based LiDAR Fiducial Marker (IILFM) system is developed. This system only requires an unstructured point cloud with intensity as the input and it has no restriction on marker placement and shape. A marker detection method that locates the predefined 3D fiducials in the point cloud through the intensity image is introduced. Then, an approach that utilizes the detected 3D fiducials to estimate the LiDAR 6-DOF pose that describes the transmission from the wor…
OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
CFAR, A*STAR（CFAR、A*STAR）；Chang’an University（长安大学）；Hefei University of Technology（合肥工业大学）；Zhongguancun Laboratory（中关村实验室）；Tianjin University（天津大学）；University of Warwick（沃里克大学）
Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing Intensity Textures
Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；Global Institute of Future Technology, Shanghai Jiao Tong University（上海交通大学全球未来技术研究院）
Constructing a point cloud for a large geographic region, such as a state or country, can require multiple years of effort. Often several vendors will be used to acquire LiDAR data, and a single region may be captured by multiple LiDAR scans. A key challenge is maintaining consistency between these scans, which includes point density, number of returns, and intensity. Intensity in particular can be very different between scans, even in areas that are overlapping. Harmonizing the intensity between scans to remove these discrepancies is expensive and time consuming. In this paper, we propose a n…
This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a full-scale model of the chessboard and fit it to the segmented 3D points of the chessboard. The model is fitted by optimizing the cost function under constraints of correlation between the reflectance intensity of laser and the color of the chessboar…
Rutgers, The State University of New Jersey（罗格斯大学，新泽西州立大学）
In this paper, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into new data structures to store the information of range, azimuth, and intensity. Then, the Dynamic Mode Decomposition method is applied to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds based on intensity channel pattern recognition. The Coarse Fine…
Uber Advanced Technologies Group（优步先进技术集团）；University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）
We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams. Our model exploits spatio-temporal relationships across multiple LiDAR sweeps to reduce the bitrate of both geometry and intensity values. Towards this goal, we propose a novel conditional entropy model that models the probabilities of the octree symbols by considering both coarse level geometry and previous sweeps' geometric and intensity information. We then use the learned probability to encode the full data stream into a compact one. Our experiments demonstrate that our method significantly redu…
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM). It is often tackled with light detection and ranging (LiDAR) sensor due to its view-point and illumination invariant properties. Existing works on 3D loop closure detection often leverage the matching of local or global geometrical-only descriptors, but without considering the intensity reading. In this paper we explore the intensity property from LiDAR scan and show that it can be effective for place recognition. Concretely, we propose a novel global descriptor, intensity scan conte…
Microsoft Research（微软研究院）；Microsoft Mixed Reality & AI Lab（微软混合现实与人工智能实验室）；École polytechnique fédérale de Lausanne（洛桑联邦理工学院）
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We propose a model that learns a mapping between RGB images and corresponding LiDAR features such as raydrop or per-point intensities directly from real datasets. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces or high intensity returns on reflective mat…
Università degli Studi di Milano - Bicocca（米兰比可卡大学）；Universidad de Alcalá（阿尔卡拉大学）；Albert-Ludwigs-Universität Freiburg（阿尔伯特-路德维希-弗莱堡大学）
In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07deg median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17deg. To the best of ou…
