Radar Sensor-Based Ego-Motion Estimation and Indoor Environment Mapping

In this article, we propose a method for performing the simultaneous localization and mapping (SLAM) in indoor environments using only multiple-input and multiple-output (MIMO) frequency-modulated continuous wave (FMCW) radar. The SLAM is a technology that maps the surrounding environment and position of a platform simultaneously. The following are the steps of the overall the SLAM implementation of the proposed method. First, the ego-velocity of the platform is estimated using the relative velocity with respect to stationary objects. In this case, a random sample consensus (RANSAC) algorithm is used in the velocity–angle map generated from the detection results to identify stationary objects. Second, the rotation angle of the platform is estimated using the linear component extracted from the walls in the
${xy}$
plane. Then, we determine the ego-motion of the platform using the estimated ego-velocity and rotation angle. Finally, we map the position of the platform and the indoor environment simultaneously in the absolute coordinate system. We validate the mapping result generated using our proposed method by comparing it with the ground truth-based mapping result.