Скачать или смотреть [GP-SLAM] Pose Graph-Based RGB-D SLAM in Low Dynamic Environments (Experiment #2)

Solution to the SLAM Problem in Low Dynamic Environments Using a Pose Graph and an RGB-D Sensor (Experiment #2)

GP-SLAM: Grouping nodes and Pruning constraints SLAM

In this study, we propose a solution to the simultaneous localization and mapping (SLAM) problem in low dynamic environments by using a pose graph and an RGB-D (red-green-blue depth) sensor. The low dynamic environments refer to situations in which the positions of objects change over long intervals. Therefore, in the low dynamic environments, robots have difficulty recognizing the repositioning of objects unlike in highly dynamic environments in which relatively fast-moving objects can be detected using a variety of moving object detection algorithms. The changes in the environments then cause false loop closing, which means that conventional SLAM algorithms produce incorrect results. To address this problem, we propose a novel SLAM method that handles low dynamic environments. The proposed method uses a pose graph structure and an RGB-D sensor. First, nodes of the graph, that represent robot poses, are grouped according to the grouping rules with noise covariances. And, false constraints of the pose graph are pruned by an error metric based on the grouped nodes. The pose graph structure is reoptimized after eliminating the false information, and the corrected localization and mapping results are obtained. The performance of the method was validated in real experiments using a mobile robot system.

And this study will be represented at ICRA 2014 Workshop on Long Term Autonomy.
https://sites.google.com/site/icra201...