Conjugate Unscented FastSLAM for Autonomous Mobile Robots in Large-Scale Environments
Map learning and self-localization based only on a perception of an environments structure are fundamental cognitive capacities required for intelligent robots to realize true autonomy. Simultaneous localization and mapping (SLAM) is an effective technique for such robots, as it addresses the problem of incrementally building an environment map from noisy sensory data and tracking the robots pose with the built map. While the Rao-Blackwellized particle filter (RBPF) is a popular SLAM technique, it tends to accumulate errors introduced by inaccurate linearization of the SLAM nonlinear function. Accordingly, RBPF-SLAM will usually fail to close large loops when applied to large-scale environments. To overcome this drawback, a new Jacobian-free RBPF-SLAM algorithm is derived in this paper. The main contribution of the algorithm lies in the utilization of the 5th-order conjugate unscented transform, which calculates the SLAM transition density up to the 5th order, to give a better distribution of the particle filter and discover local features and landmarks. The performance of the proposed SLAM is investigated and compared with that of FastSLAM2.0 and UFastSLAM in both indoor and outdoor experiments. The results verify that the proposed algorithm improves the SLAM performance in large-scale environments.
large-scale environments simultaneous localization and mapping particle filter conjugate unscented transform