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Particle filter — Scan matching SLAM recovery under kinematic model failures

机译:粒子过滤器—运动模型故障下的扫描匹配SLAM恢复

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Simultaneous localization and mapping comprises two highly correlated procedures, which renders it a greatly difficult problem. Its difficulty is further increased due to the high levels of uncertainty introduced by both the environment and the robot's sensors and actuators. In the current paper we present methods to recover from extreme situations where kinematic model failures are observed - robot slipping and movement obstruction - that result in erroneous localizations, leading to complete SLAM method failures.
机译:同时的定位和映射包括两个高度相关的过程,这使其成为一个非常困难的问题。由于环境以及机器人的传感器和执行器带来的高度不确定性,其难度进一步增加。在当前的论文中,我们提出了从极端情况下恢复的方法,在极端情况下,观察到运动学模型失败-机器人滑行和运动受阻-导致错误的定位,从而导致完全SLAM方法失败。

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