Adaptive computational SLAM incorporating strategies of exploration and path planning

Simultaneous localization and mapping (SLAM) is a well-known and fundamental topic for autonomous robot navigation. Existing solutions include the FastSLAM family-based approaches which are based on Rao–Blackwellized particle filter. The FastSLAM methods slow down greatly when the number of landmarks becomes large. Furthermore, the FastSLAM methods use a fixed number of particles, which may result in either not enough algorithms to find a solution in complex domains or too many particles and hence wasted computation for simple domains. These issues result in reduced performance of the FastSLAM algorithms, especially on embedded devices with limited computational capabilities, such as commonly used on mobile robots. To ease the computational burden, this paper proposes a modified version of FastSLAM called Adaptive Computation SLAM (ACSLAM), where particles are predicted only by odometry readings, and are updated only when an expected measurement has a maximum likelihood. As for the states of landmarks, they are also updated by the maximum likelihood. Furthermore, ACSLAM uses the effective sample size (ESS) to adapt the number of particles for the next generation. Experimental results demonstrated that the proposed ACSLAM performed 40% faster than FastSLAM 2.0 and also has higher accuracy.