Radar-Based Ego-Motion Estimation of Autonomous Robot for Simultaneous Localization and Mapping

To perform radar-based simultaneous localization and mapping, information on a robot’s ego-motion such as its rotation angle and velocity should be considered along with radar sensor data. In this paper, we propose a method to estimate the robot’s ego-motion using only a radar sensor without any other devices. To estimate the rotation angle of a robot, we use the distribution of detected points on a two-dimensional (2D) plane. The distributions of the detected points at successive time instants are correlated with each other, and the rotation angle can be estimated based on this correlation. In addition, the moving velocity of the robot is estimated from the trend line formed by the detected points on the 2D plane. To evaluate the performance of the proposed estimation method, the information about the robot’s ego-motion received from the robot motor is compared with the information estimated from the radar sensor data. The comparison of the yaw rate obtained through each method shows that our proposed method can estimate the rotation angle of the robot within an error of 3°. In addition, the ego-velocity of the radar-equipped robot can be estimated within an error of 0.073 m/s.

I. Introduction

Recently, the importance of simultaneous localization and mapping (SLAM) has been increasing due to the growing popularity of autonomous robots, vehicles and unmanned drones. SLAM is a technique in which a robot or vehicle moves in a new environment and simultaneously forms map and locates its position. To sense the surrounding environment, sensors such as laser or camera are widely used for SLAM [1]. These sensors have the advantage of detecting the distance to an object at high resolution. However, the detection performance of these sensors can be deteriorated in foggy or smoky environments [2], [3], which can significantly degrade the performance of SLAM.