粗匹配和局部尺度压缩搜索下的快速ICP-SLAM

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Title:

Fast ICP-SLAM with rough alignment and local scale-compressed searching

作者:

张金艺^1,2, 梁滨¹, 唐笛恺¹, 姚维强², 鲍深²

1. 上海大学 通信与信息工程学院, 上海 200010;
2. 上海大学 微电子研究与开发中心, 上海 200010

Author(s):

ZHANG Jinyi^1,2, LIANG Bin¹, TANG Dikai¹, YAO Weiqiang², BAO Shen²

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200010, China;
2. Microelectronic Research and Development Center, Shanghai University, Shanghai 200010, China

关键词:

ICP-SLAM; 粗匹配; 初始姿态矩阵; 局部搜索; 动态阈值; 实时性; 点云; 迭代

Keywords:

ICP-SLAM; rough alignment; initial pose matrix; local searching; dynamic threshold; real-time performance; cloud point; iteration

分类号:

TP11

DOI:

10.11992/tis.201605029

摘要:

ICP-SLAM在自主机器人和无人驾驶领域得到了极大的关注，但传统ICP-SLAM缺少当前帧和全局地图的相对位置关系，因此本文ICP算法必须经过大量的迭代之后才能达到收敛条件，这导致传统ICP-SLAM实时性很差。并且在每一次的迭代过程中，必须通过全局搜索才能完成匹配点搜索，这进一步降低了传统ICP-SLAM的实时性。为此，提出了一种快速ICP-SLAM方案。首先，通过MEMS磁力计和全局地标计算出初始位姿矩阵，通过该初始位姿矩阵实现当前帧和全局地图之间粗匹配，进而减少达到收敛条件的迭代次数。其次，在每次迭代过程中，将采用局部尺度压缩搜索完成匹配点搜索，从而减小ICP-SLAM的计算开销，提高ICP-SLAM实时性；同时，每次迭代完成之后，还将通过动态阈值缩小搜索范围，达到加快匹配点搜索的速度，进而提高ICP-SLAM实时性。实验结果表明，和传统ICP-SLAM相比，在理想室内静止场景下，快速ICP-SLAM的迭代次数最高减小了92.34%，ICP算法运行时间最高降低了98.86%。除此之外，ICP-SLAM的整体负载也被保持在可控范围内，ICP-SLAM的整体性能得到很大的提升。

Abstract:

ICP-SLAM has received much attention in the field of autonomous robots and unmanned cars. However, two deficiencies in traditional ICP-SLAM usually result in poor real-time performance. The first is the fact that the relative position between the current scan frame and the global map is not previously known. As a result, the ICP algorithm takes a large number of iterations to reach convergence. The second is that the establishment of correspondence is carried out by global searching and this requires an enormous amount of computational time. To overcome these problems, a fast ICP-SLAM is proposed. To decrease the number of iterations a rough alignment, based on an initial pose matrix, is proposed. In detail, the initial pose matrix is computed using a MEMS magnetometer and global landmarks. Then, a rough alignment is applied between the current scan frame and the global map at the beginning of the ICP algorithm with an initial pose matrix. To accelerate the establishment of correspondence, local scale-compressed searching with a dynamic threshold is proposed where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM, under ideal stable conditions, the best experimental results show amount of iteration for ICP algorithm to reach convergence reduces 92.34% and ICP algorithm runtime reduces 98.86%. In addition, computational cost is kept at a stable level due to the elimination of accumulated computational consumption. Moreover, great improvement is observed in the quality and robustness of SLAM