Simultaneous Localization and Mapping Using a Novel Dual Quaternion Particle Filter

In this paper, we present a novel approach to perform simultaneous localization and mapping (SLAM) for planar motions based on stochastic filtering with dual quaternion particles using low-cost range and gyro sensor data. Here, SE(2) states are represented by unit dual quaternions and further get stochastically modeled by a distribution from directional statistics such that particles can be generated by random sampling. To build the full SLAM system, a novel dual quaternion particle filter based on Rao-Blackwellization is proposed for the tracking block, which is further integrated with an occupancy grid mapping block. Unlike previously proposed filtering approaches, our method can perform tracking in the presence of multi-modal noise in unknown environments while giving reasonable mapping results. The approach is further evaluated using a walking robot with on-board ultrasonic sensors and an IMU sensor navigating in an unknown environment in both simulated and real-world scenarios.