Monocular Visual Odometry Benchmarking and Turn Performance Optimization

Abstract

Developing ground robots for crop monitoring and harvesting in steep slope vineyards is a complex challenge due to two main reasons: harsh condition of the terrain and unstable localization accuracy obtained with Global Navigation Satellite System. In this context, a reliable localization system requires an accurate and redundant information to Global Navigation Satellite System and wheel odometry based system. To pursue this goal we benchmark 3 well known Visual Odometry methods with 2 datasets. Two of these are feature-based Visual Odometry algorithms: Libviso2 and SVO 2.0. The third is an appearance-based Visual Odometry algorithm called DSO. In monocular Visual Odometry, two main problems appear: pure rotations and scale estimation. In this paper, we focus on the first issue. To do so, we propose a Kalman Filter to fuse a single gyroscope with the output pose of monocular Visual Odometry, while estimating gyroscope's bias continuously. In this approach we propose a non-linear noise variation that ensures that bias estimation is not affected by Visual Odometry resultant rotations. We compare and discuss the three unchanged methods and the three methods with the proposed additional Kalman Filter. For tests, two public datasets are used: the Kitti dataset and another built in-house. Results show that our additional Kalman Filter highly improves Visual Odometry performance in rotation movements.