Dynamic Occupancy Grid Mapping is a technique used to generate a local map of the environment containing both static and dynamic information. Typically, these maps are primarily generated using lidar measurements. However, with improvements in radar sensing, resulting in better accuracy and higher resolution, radar is emerging as a viable alternative to lidar as the primary sensor for mapping. In this paper, we propose a radar-centric dynamic occupancy grid mapping algorithm with adaptations to the state computation, inverse sensor model, and field-of-view computation tailored to the specifics of radar measurements. We extensively evaluate our approach using real data to demonstrate its effectiveness and establish the first benchmark for radar-based dynamic occupancy grid mapping using the publicly available Radarscenes dataset.
Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. Recently many parking detection systems using range-based on-vehicle sensors are invented, but they disregard the practical difficulty of obtaining access to raw sensory data which are required for any feature-based algorithm. In this paper, we focus on a system using short-range radars (SRR) embedded in Advanced Driver Assistance System (ADAS) to collect occupancy information, and broadcast it through a connected vehicle network. The challenge that the data transmitted through ADAS unit has …
Scalable Radar-based Roadside Perception: Self-localization and Occupancy Heat Map for Traffic Analysis
Fraunhofer IVI（弗劳恩霍夫应用信息学研究所（IVI））；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Technische Hochschule Ingolstadt（英戈尔施塔特应用技术大学）；Technical University of Munich（慕尼黑工业大学）；Fraunhofer IOSB（弗劳恩霍夫光学、电子和图像利用研究所（IOSB））
One essential step to realize modern driver assistance technology is the accurate knowledge about the location of static objects in the environment. In this work, we use artificial neural networks to predict the occupation state of a whole scene in an end-to-end manner. This stands in contrast to the traditional approach of accumulating each detection's influence on the occupancy state and allows to learn spatial priors which can be used to interpolate the environment's occupancy state. We show that these priors make our method suitable to predict dense occupancy estimations from sparse, highl…
Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion. To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot to…
Through constant improvements in recent years radar sensors have become a viable alternative to lidar as the main distancing sensor of an autonomous vehicle. Although robust and with the possibility to directly measure the radial velocity, it brings it's own set of challenges, for which existing algorithms need to be adapted. One core algorithm of a perception system is dynamic occupancy grid mapping, which has traditionally relied on lidar. In this paper we present a dual-weight particle filter as an extension for a Bayesian occupancy grid mapping framework to allow to operate it with radar a…
National Yang Ming Chiao Tung University（阳明交通大学）；Industrial Technology Research Institute（工业技术研究院）
Radar shows great potential for autonomous driving by accomplishing long-range sensing under diverse weather conditions. But radar is also a particularly challenging sensing modality due to the radar noises. Recent works have made enormous progress in classifying free and occupied spaces in radar images by leveraging lidar label supervision. However, there are still several unsolved issues. Firstly, the sensing distance of the results is limited by the sensing range of lidar. Secondly, the performance of the results is degenerated by lidar due to the physical sensing discrepancies between the …
LiCROcc: Teach Radar for Accurate Semantic Occupancy Prediction using LiDAR and Camera
Institute of Cyber-Systems and Control, Zhejiang University（浙江大学 cyber 系统与控制研究所）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Technical University of Munich（慕尼黑工业大学）
Prediction of dynamic environment is crucial to safe navigation of an autonomous vehicle. Urban traffic scenes are particularly challenging to forecast due to complex interactions between various dynamic agents, such as vehicles and vulnerable road users. Previous approaches have used egocentric occupancy grid maps to represent and predict dynamic environments. However, these predictions suffer from blurriness, loss of scene structure at turns, and vanishing of agents over longer prediction horizon. In this work, we propose a novel framework to make long-term predictions by representing the tr…
OC-SOP: Enhancing Vision-Based 3D Semantic Occupancy Prediction by Object-Centric Awareness
Autonomous Intelligent Systems group, Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence, University of Bonn, Germany（自主智能系统组，计算机科学研究所VI——智能系统与机器人——以及机器人中心和拉玛尔人工智能与机器学习研究所，波恩大学，德国）
Motion planning for safe autonomous driving requires learning how the environment around an ego-vehicle evolves with time. Ego-centric perception of driveable regions in a scene not only changes with the motion of actors in the environment, but also with the movement of the ego-vehicle itself. Self-supervised representations proposed for large-scale planning, such as ego-centric freespace, confound these two motions, making the representation difficult to use for downstream motion planners. In this paper, we use geometric occupancy as a natural alternative to view-dependent representations suc…
Ford Werke GmbH（福特汽车有限公司）；RWTH Aachen University（亚琛工业大学）
With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied space based on radar detections is notoriously difficult because of the data sparsity and the environment dependent noise (e.g. multipath reflections). Recently, deep learning-based inverse sensor models, from here on called deep ISMs, have been shown to improve over their geometric counterparts in retrieving occupancy information. Nevertheless, these methods perform a data-driven interpolation which has to be verifie…
Mercedes-Benz RD/ASF（梅赛德斯-奔驰研发/自动驾驶安全基础部门）；University of Pennsylvania（宾夕法尼亚大学）；Fraunhofer FKIE（弗劳恩霍夫通信、信息处理和人机工程学研究所）
Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examined because of the increased complexity of dynamic grids concerning both velocity measurement models for radar sensors and the representation of velocity in a grid framework. In this paper, both issues are addressed: sensor models and an efficient grid framework, which are required to ensure efficient and robust environment perception with radar. To that, we introduce new inverse radar sensor models covering radar sen…
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
State Key Laboratory of Ocean Sensing, Zhejiang University（浙江大学海洋传感国家重点实验室）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（浙江大学海洋交叉学科学生培养平台）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of AI and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）
Mercedes-Benz Research & Development North America, Inc.（梅赛德斯-奔驰北美研发有限公司）；Institute of Measurement, Control, and Microtechnology, Ulm University（乌尔姆大学测量、控制与微技术研究所）；Ulm University（乌尔姆大学）
Grid mapping is a well established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot's environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter (BBF). A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline pr…
Future urban transportation concepts include a mixture of ground and air vehicles with varying degrees of autonomy in a congested environment. In such dynamic environments, occupancy maps alone are not sufficient for safe path planning. Safe and efficient transportation requires reasoning about the 3D flow of traffic and properly modeling uncertainty. Several different approaches can be taken for developing 3D velocity maps. This paper explores a Bayesian approach that captures our uncertainty in the map given training data. The approach involves projecting spatial coordinates into a high-dime…
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajectories by using a highly accurate hand-held global navigation satellite system (GNSS). An embedded inertial measurement unit (IMU) is used for estimating orientation and motion behavior. This article contains two major contributions. A method for associating radar data to vulnerable…
Robert Bosch GmbH（罗伯特·博世有限公司）；Bosch (China) Investment Co. Ltd.（博世（中国）投资有限公司）
Sensor visibility is crucial for safety-critical applications in automotive, robotics, smart infrastructure and others: In addition to object detection and occupancy mapping, visibility describes where a sensor can potentially measure or is blind. This knowledge can enhance functional safety and perception algorithms or optimize sensor topologies. Despite its significance, to the best of our knowledge, neither a common definition of visibility nor performance metrics exist yet. We close this gap and provide a definition of visibility, derived from a use case review. We introduce metrics and a …
