State Estimation Approach for Depth Sensor's Posture Attached to a Vehicle or a Human Body

Abstract

The author has been developing a navigation system for safety for mobile robots, mobility scooters and pedestrians by using depth sensors which can capture range data of image size. If the geometrical relation between the sensor and the space is completely known, each point of the captured range data can be classified into three groups: upper than the ground, on the ground, and lower than the ground. However, it is essential to be able to deal with the unpredictable change of the posture of the sensor due to the movement of the attached body. The author already developed a real-time estimation scheme of the posture including pitch angle, roll angle, and height from the observed data in the framework of optimization. In this paper, the author proposes an estimation scheme based on the state space model and apply Extended Kalman filter for the same application problem. We will compare the algorithms by experimental results and will show the usefulness of the proposed algorithm.