引言 Direct LiDAR Odometry: Fast Localization with Dense Point Clouds (DLO) DLO是一个轻量级且计算高效的前端 LiDAR 里程计解决方案,具有一致且准确的定位。它具有多项算法创新,可提高感知挑战性环境中姿态估计的速度、准确性和鲁棒性,并已在空中和腿式机器人上进行了广泛的测试。 这项工
《Direct LiDAR Odometry: Fast Localization with Dense Point Clouds》( arXiv:2110.00605 ) Motivation 就目前而言,以LOAM为依据的激光里程计算法都很难满足能够高速实时处理密集点云的要求。所以在这个工作里提出了能够实现高速高精度LO的思路。 Contribution 提出了一个定制的速度优先的处理流程,这个流程可以实时精确...
Direct LiDAR Odometry: Fast Localization with Dense Point Clouds [IEEE RA-L] [ArXiv] [Video] [Code] DLO is a lightweight and computationally-efficient frontend LiDAR odometry solution with consistent and accurate localization. It features several algorithmic innovations that increase speed, accuracy,...
The common challenges of LiDAR odometry are low data rate, the requirement of LiDAR pose information, and motion distortion due to scan rates. VLOAM architecture bridges over the weaknesses of both visual and LiDAR sensors allowing accurate estimation, mapping, and localization. This online odometry...
Keywords Mobile ground robot Lidar-based localization SLAM Real time View PDFReferences 1 Akai, N., Morales, L.Y., Takeuchi, E., Yoshihara, Y., Ninomiya, Y., (2017. Robust localization using 3d ndt scan matching with experimentally determined uncertainty and road marker matching, in: 2017 ...
Global localization can effectively solve the problem of vehicle re-localization. However, it still has the following issues: (1) Dense point cloud maps require large memory costs. (2) While odometry solutions can provide initial guesses, they are severely affected by sparse LiDAR point densities ...
LiDAR-based localization in a city-scale map is a fundamental question in autonomous driving research. As a reasonable localization scheme, the localization can be performed by global retrieval (that suggests potential candidates from the database) followed by geometric registration (that obtains an ac...
Simultaneous Localization and Mapping (SLAM) algorithms play a crucial role in automated vehicles. These vehicles utilize Light Detection and Ranging (LiDAR), Radio Detection and Ranging (RADAR) and camera sensors to perceive their surroundings and use the onboard sensor data to determine their locati...
Self-localization Abstract map Lidar Velodyne Planar surface map Vector map Urban area 1. Introduction Autonomous vehicle technology has been considered as one of the key components of the intelligent transportation systems since their first introduction. In recent years, significant progress has been mad...
The proposed module is developed using LiDAR point cloud data and can be integrated into a visual LiDAR odometry and mapping pipeline implemented in the vehicle. “ConvPoint,” the top-performing neural network architecture in an online point cloud segmentation benchmark leaderboard at the time of ...