Real-Time Visual Odometry by Patch Tracking Using GPU-Based Perspective Calibration.

In this paper we describe VOPT (Visual Odometry by Patch Tracking), a robust algorithm for visual odometry, which is able to operate with sparse or dense maps computed by simultaneous localization and mapping (SLAM) algorithms. By using an iterative multi-scale procedure, VOPT is able to estimate the individual motion, photometric correction and reliability tracking confidence of a set of planar patches. In order to overcome the high computational cost of the patch adjustment, we use a GPU-based least-square solver, achieving real-time performance. The algorithm can also be used as a building block to other procedures for automatic initialization and recovery of 3D scene. Our tests show that VOPT outperforms the well-known PTAMM and the state-of-art ORB-SLAM algorithm in challenging videos using the same input maps.