AN IMPROVED SIMULTANEOUS LOCALIZATION AND MAPPING FOR DYNAMIC ENVIRONMENTS

Abstract

For most of the proposed methods for multi-robot simultaneous localization and mapping (MRSLAM), a basic assumption is a static environment. However, in reality, the dynamics of the environment degrade the performance and might lead to a complete failure. Thus, the discrimination between dynamic and static parts of the environment is a key aspect. In this paper, a novel cooperative approach named parallel map is introduced to improve the performance of SLAM in dynamic environments in the threedimensional (3D) space. The 3D map of point features is extracted using the acquired pictures. Based on the relative positions of the landmarks, a probabilistic index is generated to detect and prevent the inclusion of dynamics in the back-end estimations. A cooperative MRSLAM is used to acquire data from the robots and merge local maps. To evaluate the performance, simulations are applied and accuracy, robustness, and computational resources are reported. Results indicate that the proposed method is feasible for MRSLAM in dynamic environments in 3D spaces