MOSAIC: Simultaneous Localization and Environment Mapping Using mmWave Without A-Priori Knowledge

Abstract

Simultaneous localization and environment mapping (SLAM) is the core to robotic mapping and navigation as it constructs simultaneously the unknown environment and localizes the agent within. However, in millimeter wave (mmWave) research, SLAM is still at its infancy. This paper consists a first of its kind in mapping an indoor environment based on the RSS, Time-Difference-of-Arrival, and Angle-of-Arrival measurements. We introduce MOSAIC as a new approach for SLAM in indoor environment by exploiting the map-based channel model. More precisely, we perform localization and environment inference through obstacle detection and dimensioning. The concept of virtual anchor nodes (VANs), known in literature as the mirrors of the real anchors with respect to the obstacles in the environment, is explored. Then, based on these VANs, the obstacles positions and dimensions are estimated by detecting the zone of paths obstruction, points of reflection, and obstacle vertices. Then, extended Kalman filter is adapted to the studied environment to improve the estimation of the points of reflection hence the mapping accuracy. Cramer–Rao lower bounds are also derived to find the optimal number of anchor nodes. Simulation results have shown high localization accuracy and obstacle detection using mmWave technology.