Image-Based ICP Algorithm for Visual Odometry Using a RGB-D Sensor in a Dynamic Environment

This paper proposes a novel approach to calculate visual odometry using Microsoft Kinect incorporating depth information into RGB color information to generate 3D feature points based on speed up robust features (SURF) descriptor. In particular, the generated 3D feature points are used for calculating the iterative closest point (ICP) algorithm between successive images from the sensor. The ICP algorithm works based on image information of features differently from previous approaches. This paper suggests one of the modified versions for a state-of-the-art implementation of the ICP algorithm. Such an approach makes accurate calculation of the rigid body transformation matrix for visual odometry in a dynamic environment. From this calculation step, dynamically moving features can be separated into outliers. Then, the outliers are filtered with random sample consensus (RANSAC) algorithm for accurate calculation of the rigid body transformation matrix. The experiments demonstrate that visual odometry is successfully obtained using the proposed algorithm in a dynamic environment.