Semi-direct visual odometry for a fisheye-stereo camera

Abstract

We present a semi-direct visual odometry algorithm for a fisheye-stereo camera. In a tracking thread, we simultaneously track oriented patches and estimate the camera pose. In a mapping thread, we estimate the coordinates and surface normal for each new patch to be tracked. Estimation of the surface normals allows us to track patches over a wide variety of viewpoints. In our algorithm, we do not make use of descriptors and robust descriptor matching to find patch correspondences. Instead, we use photoconsistency-based techniques to find patch correspondences. For tracking, we use sparse model-based image alignment to find the relative motion estimate, and feature alignment to find 2D-3D patch correspondences. For mapping, we use plane-sweeping stereo to find matching patches between stereo images. We also implement a state estimator based on the Extended Kalman Filter (EKF) to fuse inertial measurements and relative pose estimates from our visual odometry implementation. We run experiments in two different outdoor environments to validate our algorithm, and discuss the experimental results. Our implementation runs at an average of 42 Hz on a commodity Intel CPU. To the best of our knowledge, there is no other existing semi-direct visual odometry algorithm for a fisheye-stereo camera.