A Simultaneous Localization and Mapping Algorithm in Complex Environments: SLASEM

Rongchuan Sun, Shugen Ma, Bin Li, Minghui Wang, Yuechao Wang
2011 Advanced Robotics  
In this paper we present an algorithm for the application of simultaneous localization and mapping in complex environments. Instead of building a grid map or building a feature map with a small number of the obstacles' geometric parameters, the proposed algorithm builds a sampled environment map (SEM) to represent a complex environment with a set of environment samples. To overcome the lack of one-toone correspondence between environment samples and raw observations, the signed orthogonal
more » ... ce function is proposed to be used as the observation model. A method considering geometric constraints is presented to remove redundant environment samples from the SEM. We also present a method to improve the SEM's topological consistency by using corner constraints. The proposed algorithm has been verified in a simulation and an indoor experiment. The results show that the algorithm can localize the robot and build a complex map effectively.
doi:10.1163/016918611x563373 fatcat:kqsha4wrhremzcuhqcb3ssqy6e