Visual Odometry Algorithm Using an RGB-D Sensor and IMU in a Highly Dynamic Environment

Abstract

This paper proposes a robust visual odometry algorithm using a Kinect-style RGB-D sensor and inertial measurement unit (IMU) in a highly dynamic environment. Based on SURF (Speed Up Robust Features) descriptor, the proposed algorithm generates 3-D feature points incorporating depth information into RGB color information. By using an IMU, the generated 3-D feature points are rotated in order to have the same rigid body rotation component between two consecutive images. Before calculating the rigid body transformation matrix between the successive images from the RGB-D sensor, the generated 3-D feature points are filtered into dynamic or static feature points using motion vectors. Using the static feature points, the rigid body transformation matrix is finally computed by RANSAC (RANdom SAmple Consensus) algorithm. The experiments demonstrate that visual odometry is successfully obtained for a subject and a mobile robot by the proposed algorithm in a highly dynamic environment. The comparative study between proposed method and conventional visual odometry algorithm clearly show the reliability of the approach for computing visual odometry in a highly dynamic environment.KeywordsMobile RobotFeature PointGraphic Processing UnitMotion VectorIterative Close PointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.