Abstract
Fast simultaneous localization and mapping (FastSLAM) is one of the most popular methods for autonomous navigation of mobile robots. However, FastSLAM is essentially a particle filter (PF) that suffers from particle impoverishment and degeneracy problems. To improve its localization performance, this paper proposes an improved FastSLAM algorithm that contains an intelligent bat-inspired resampling whose iteration times can be adaptively tuned based on the degree of filter diverging. Additionally, the square root cubature filter is merged into the algorithm for better proposal distribution and mapping results. The advantages of the proposed method are verified by simulation and dataset-based tests. The test result demonstrates that the proposed IFastSLAM has better accuracy, computational efficiency and filter consistency compared to that of the square root unscented FastSLAM (SRUFastSLAM) and strong tracking square root central difference FastSLAM (STSRCDFastSLAM). Finally, a pool experiment is demonstrated to further verify the advantages of the proposed algorithm.
Highlights
Simultaneous localization and mapping (SLAM), referred to as concurrent mapping and localization (CML), is a popular method to improve localization accuracy of mobile robots
OPTIMIZATION PROCESS OF PARTICLES IN IFASTSLAM The schematic diagram of improving particle distribution in IFastSLAM is demonstrated in Fig.3, in which a larger size and darker color particle corresponds to a particle that has larger weight
Compared to the conventional methods by sampling from the prior distribution [27], [28], IFastSLAM samples from the proposal distribution generated by square root cubature Kalman filter (SRCKF), and the particles can tend to a higher posterior PDF region
Summary
Simultaneous localization and mapping (SLAM), referred to as concurrent mapping and localization (CML), is a popular method to improve localization accuracy of mobile robots. The computational burdens of computing Jacobian matrixes with the growing map size restricts the algorithm to large-scale environments To overcome these defects, a number of particle filter-based SLAM algorithms have been proposed [4]–[6]. A resampling step is generally followed by the importance sampling step in FastSLAM to improve the algorithm performance [13]. The common resampling methods, such as the systematic resampling, multinomial resampling and the residual resampling, have proved to be well-performed in preventing deterioration of particle filters Their redundant particles would lower diversity of the particles and cause imprecise approximation of the robot state. In the proposed FastSLAM algorithm, the square root cubature Kalman filter (SRCKF) is fused for stepwise update of robot poses and map features [15], instead of updating in an augmented way [16].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
