Online simultaneous localization and mapping with parallelization for dynamic line segments based on moving horizon estimation
Online simultaneous localization and mapping with parallelization for dynamic line segments based on moving horizon estimation
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DOI：
10.1007/s10015-024-00937-8
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发表时间：
2024-03
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影响因子：
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通讯作者：
Haziq Muhammad;Yasumasa Ishikawa;Kazuma Sekiguchi;Kenichiro Nonaka
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作者：
Haziq Muhammad;Yasumasa Ishikawa;Kazuma Sekiguchi;Kenichiro Nonaka
在本文中，为了使SLAM在动态环境中具有鲁棒性，我们提出了一种新的激光雷达SLAM算法，该算法通过移动地平线估计（MHE）来估计场景中所有物体的速度，同时抑制静态物体的速度。我们把环境特征近似为具有速度的动态线段。为了处理静态对象，也采用了MHE，因此其目标函数允许添加处理静态对象的速度抑制项。SLAM算法通过考虑关联概率，跟踪线段的端点，估计沿线段的速度。即使它暂时被遮挡，估计也是准确的，因为MHE考虑了过去测量的有限长度。机器人的定位与地图估计的并行化以及对决策变量的仔细数学消除允许在线实现。后期处理修改通过考虑激光雷达激光的穿透和整合地图来消除可能的虚假估计。仿真和实验结果表明，该方法可以在存在运动目标的情况下鲁棒地进行在线SLAM。
In this paper, to render SLAM robust in dynamic environments, we propose a novel LiDAR SLAM algorithm that estimates the velocity of all objects in the scene while suppressing speed of static objects by moving horizon estimation (MHE). We approximate environment features as dynamic line segments having velocity. To deal with static objects as well, MHE is employed, so that its objective function allows the addition of velocity suppression terms that treat stationary objects. By considering association probability, the SLAM algorithm can track the endpoints of line segments to estimate the velocity along the line segments. Even if it is temporarily occluded, the estimation is accurate, because MHE considers a finite length of past measurements. Parallelization of the robot’s localization with the map’s estimation and careful mathematical elimination of decision variables allows online implementations. Post-process modifications remove possible spurious estimates by considering the piercing of LiDAR lasers and integrating maps. Simulation and experiment results of the proposed method prove that the presented algorithm can robustly perform online SLAM even with moving objects present.