CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
复制标题
CT-ICP：具有环路闭合功能的实时弹性 LiDAR 里程计
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Franccois Goulette
中科院分区：
文献类型：
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作者：
Pierre Dellenbach;Jean;Bastien Jacquet;Franccois Goulette
多光束激光雷达传感器越来越多地用于机器人技术，特别是用于自动汽车的定位和感知任务，两者都依赖于构建精确环境地图的能力。为此，我们提出了一种新的实时激光雷达测距方法，称为CT-ICP（连续时间ICP），完成了一个完整的SLAM与一个新的循环检测过程。该方法的核心是引入扫描匹配中的连续性和扫描间的不连续性。它允许在配准期间扫描的弹性失真以提高精度，以及对来自不连续性的高频运动的增强的鲁棒性。我们建立了一个完整的SLAM上的里程计，使用一个快速的纯激光雷达环检测的基础上海拔图像的2D匹配，提供一个姿态图与循环约束。为了证明该方法的鲁棒性，我们在七个数据集上进行了测试：KITTI，KITTI-raw，KITTI-360，KITTICARLA，ParisLuco，Newer College和NCLT驾驶和高频运动场景。CT-ICP里程计和线圈检测都可以在线使用。CT-ICP目前在KITTI里程计排行榜上排名第一，平均相对翻译误差（RTE）为0.59%，单线程CPU上每次扫描的平均时间为60 ms。
Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment. For this, we propose a new real-time LiDAR-only odometry method called CT-ICP (for Continuous-Time ICP), completed into a full SLAM with a novel loop detection procedure. The core of this method, is the introduction of the combined continuity in the scan matching, and discontinuity between scans. It allows both the elastic distortion of the scan during the registration for increased precision, and the increased robustness to high frequency motions from the discontinuity. We build a complete SLAM on top of this odometry, using a fast pure LiDAR loop detection based on elevation image 2D matching, providing a pose graph with loop constraints. To show the robustness of the method, we tested it on seven datasets: KITTI, KITTI-raw, KITTI-360, KITTICARLA, ParisLuco, Newer College, and NCLT in driving and high-frequency motion scenarios. Both the CT-ICP odometry and the loop detection are made available online. CT-ICP is currently first, among those giving access to a public code, on the KITTI odometry leaderboard, with an average Relative Translation Error (RTE) of 0.59% and an average time per scan of 60ms on a CPU with a single thread.
DOI：
10.15607/rss.2018.xiv.016
发表时间：
2018-06
期刊：
Robotics: Science and Systems XIV
影响因子：
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作者：
J. Behley;C. Stachniss
通讯作者：
J. Behley;C. Stachniss