Robust Data Association for Object-level Semantic SLAM
This addresses SLAM robustness issues in textureless or cluttered indoor environments for robotics applications, representing an incremental improvement over existing methods.
The paper tackles the challenge of simultaneous mapping and localization (SLAM) in real indoor environments by proposing a compact semantic SLAM framework that combines geometric and object-level semantic constraints. Experiments on public datasets show improved robustness and accuracy compared to other SLAM systems.
Simultaneous mapping and localization (SLAM) in an real indoor environment is still a challenging task. Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines, which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects. In this paper, a compact semantic SLAM framework is proposed, with utilization of both geometric and object-level semantic constraints jointly, a more consistent mapping result, and more accurate pose estimation can be obtained. Two main contributions are presented int the paper, a) a robust and efficient SLAM data association and optimization framework is proposed, it models both discrete semantic labeling and continuous pose. b) a compact map representation, combining 2D Lidar map with object detection is presented. Experiments on public indoor datasets, TUM-RGBD, ICL-NUIM, and our own collected datasets prove the improving of SLAM robustness and accuracy compared to other popular SLAM systems, meanwhile a map maintenance efficiency can be achieved.