A geometric nonlinear stochastic filter for simultaneous localization and mapping

Simultaneous Localization and Mapping (SLAM) is one of the key robotics tasks as it tackles simultaneous mapping of the unknown environment defined by multiple landmark positions and localization of the unknown pose (i.e., attitude and position) of the robot in three-dimensional (3D) space. The true SLAM problem is modeled on the Lie group of SLAM(n)(3), and its true dynamics rely on angular and translational velocities. This paper proposes a novel geometric nonlinear stochastic estimator algorithm for SLAM on SLAM(n)(3) that precisely mimics the nonlinear motion dynamics of the true SLAM problem. Unlike existing solutions, the proposed stochastic filter takes into account unknown constant bias and noise attached to the velocity measurements. The proposed nonlinear stochastic estimator on manifold is guaranteed to produce good results provided with the measurements of angular velocities, translational velocities, landmarks, and inertial measurement unit (IMU). Simulation and experimental results reflect the ability of the proposed filter to successfully estimate the six-degrees-of-freedom (6 DoF) robot's pose and landmark positions. (C) 2021 Elsevier Masson SAS. All rights reserved.

Automated summary

The paper introduces a novel geometric nonlinear stochastic estimator algorithm for the SLAM problem on SLAM(n)(3), which accurately mimics the nonlinear motion dynamics of the true SLAM problem and successfully estimates the robot's pose and landmark positions.