里程计
计算机科学
惯性测量装置
人工智能
激光雷达
稳健性(进化)
比例(比率)
树(集合论)
计算机视觉
数学
遥感
地理
地图学
组合数学
生物化学
化学
机器人
基因
移动机器人
作者
Thien‐Minh Nguyen,Daniel Duberg,Patric Jensfelt,Shenghai Yuan,Lihua Xie
出处
期刊:IEEE robotics and automation letters
日期:2023-04-01
卷期号:8 (4): 2102-2109
被引量:6
标识
DOI:10.1109/lra.2023.3246390
摘要
While feature association to a global map has significant benefits, to keep the computations from growing exponentially, most lidar-based odometry and mapping methods opt to associate features with local maps at one voxel scale. Taking advantage of the fact that surfels (surface elements) at different voxel scales can be organized in a tree-like structure, we propose an octree-based global map of multi-scale surfels that can be updated incrementally. This alleviates the need for recalculating, for example, a k-d tree of the whole map repeatedly. The system can also take input from a single or a number of sensors, reinforcing the robustness in degenerate cases. We also propose a point-to-surfel (PTS) association scheme, continuous-time optimization on PTS and IMU preintegration factors, along with loop closure and bundle adjustment, making a complete framework for Lidar-Inertial continuous-time odometry and mapping. Experiments on public and in-house datasets demonstrate the advantages of our system compared to other state-of-the-art methods.
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