Visual odometry priors for robust EKF-SLAM

Abstract

One of the main drawbacks of standard visual EKF-SLAM techniques is the assumption of a general camera motion model. Usually this motion model has been implemented in the literature as a constant linear and angular velocity model. Because of this, most approaches cannot deal with sudden camera movements, causing them to lose accurate camera pose and leading to a corrupted 3D scene map. In this work we propose increasing the robustness of EKF-SLAM techniques by replacing this general motion model with a visual odometry prior, which provides a real-time relative pose prior by tracking many hundreds of features from frame to frame. We perform fast pose estimation using the two-stage RANSAC-based approach from [1]: a two-point algorithm for rotation followed by a one-point algorithm for translation. Then we integrate the estimated relative pose into the prediction step of the EKF. In the measurement update step, we only incorporate a much smaller number of landmarks into the 3D map to maintain real-time operation. Incorporating the visual odometry prior in the EKF process yields better and more robust localization and mapping results when compared to the constant linear and angular velocity model case. Our experimental results, using a handheld stereo camera as the only sensor, clearly show the benefits of our method against the standard constant velocity model.