For SLAM to be safely deployed in unstructured real world environments, it must possess several key properties that are not encompassed by conventional benchmarks. In this paper we show that SLAM commutativity, that is, consistency in trajectory estimates on forward and reverse traverses of the same route, is a significant issue for the state of the art. Current pipelines show a significant bias between forward and reverse directions of travel, that is in addition inconsistent regarding which direction of travel exhibits better performance. In this paper we propose several contributions to feature-based SLAM pipelines that remedies the motion bias problem. In a comprehensive evaluation across four datasets, we show that our contributions implemented in ORB-SLAM2 substantially reduce the bias between forward and backward motion and additionally improve the aggregated trajectory error. Removing the SLAM motion bias has significant relevance for the wide range of robotics and computer vision applications where performance consistency is important.

通过在四个数据集上进行综合评估，我们展示了我们在ORB-SLAM2中实施的多项贡献大大减少了前进和后退运动之间的偏差，并且进一步改善了汇总的轨迹误差，消除SLAM运动偏差对于广泛的机器人和计算机视觉应用的性能一致性非常重要。

无运动偏差的特征基 SLAM