Purpose

The light detection and ranging sensor has been widely deployed in the area of simultaneous localization and mapping (SLAM) for its remarkable accuracy, but obvious drift phenomenon and large accumulated error are inevitable when using SLAM. The purpose of this study is to alleviate the accumulated error and drift phenomenon in the process of mapping.

Design/methodology/approach

A novel light detection and ranging SLAM system is introduced based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies conditions of loop-closed.

Findings

The proposed algorithm exhibits competitiveness compared with current approaches in terms of the accumulated error and drift distance. Further, supplementary to the place recognition process that is usually performed for loop detection, the authors introduce a novel dynamic constraint that takes into account the change in the direction of the robot throughout the total path trajectory between corresponding frames, which contributes to avoiding potential misidentifications and improving the efficiency.

Originality/value

The proposed system is based on Normal Distributions Transform and dynamic Scan Context with switch. The pose-graph optimization is used as back-end optimization module. The loop closure detection is only operated in the scenario, while the path satisfies condition of loop-closed.

中文翻译：

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ODLC_SAM：一种面向露天环境的新型 LiDAR SLAM 系统，具有闭环功能

目的

光探测测距传感器以其卓越的精度在同步定位与建图（SLAM）领域得到了广泛的应用，但在使用SLAM时不可避免地会出现明显的漂移现象和较大的累积误差。本研究的目的是缓解测绘过程中的累积误差和漂移现象。

设计/方法论/途径

介绍了一种基于正态分布变换和带开关的动态扫描上下文的新型光检测和测距 SLAM 系统。位姿图优化用作后端优化模块。闭环检测仅在路径满足闭环条件的场景下进行。

发现

与现有方法相比，该算法在累积误差和漂移距离方面表现出竞争力。此外，作为对循环检测通常执行的位置识别过程的补充，作者引入了一种新颖的动态约束，该约束考虑了机器人在相应帧之间的整个路径轨迹中的方向变化，这有助于避免潜在的错误识别并提高效率。

原创性/价值

所提出的系统基于正态分布变换和带开关的动态扫描上下文。位姿图优化用作后端优化模块。闭环检测仅在路径满足闭环条件的场景下进行。