VERIFICATION OF GRID BASED FASTSLAM WITH MULTIPLE CANDIDATES OF PARTICLES

In this paper, we propose a strategy for improving the accuracy of own positions in FastSLAM (Fast-Simultaneous Localization And Mapping). One can see that estimation of self-position and mapping are important for an autonomous mobile robot. As one approach to do this, GPS (Global Positioning System) based self-positioning methods have been proposed. However, GPS-based method has disadvantage that positioning is difficult in the environment where radio waves cannot be received. On the other hand, it is well known that SLAM can perform self-positioning by sensing the external environment based on LiDAR sensors, cameras and other devices. Since SLAM does not require online communication, estimate of self-position and mapping can be performed without being affected by the driving environment. FastSLAM is one of the SLAM algorithms, and samples the path using a particle filter. Each particle represents one possible motion path of robots in the particle filter, and the observed information is used to calculate the weight of each particle and evaluate each path. In FastSLAM, when self-location is determined, a particle is selected by comparing its likelihood as evaluated by the likelihood function. Since this depends on observed information, the reliability of the estimate depends on the accuracy of the sensor. Thus, there is possibility that there are more useful particles among those not selected. In this paper, we propose a new FastSLAM algorithm that selects multiple candidates of particles in FastSLAM algorithm. In the proposed approach, multiple candidates are stored and estimates of self-position is determined by using multiple candidates in the next step. By maintaining multiple particles, more robust self-position estimation and mapping can be performed comparing with the conventional FastSLAM. In this paper, we show the proposed algorithm and numerical simulations are shown to evaluate the performance of the proposed algorithm.