Accurate and Robust Stereo Direct Visual Odometry for Agricultural Environment

Vision-based localization and mapping in the agricultural environment is challenging due to the unstructured scene with unstable features, illumination variations, bumpy roads, and dynamic environmental objects. To address these challenges, we propose an accurate and robust stereo direct visual odometry system with modifications on Stereo-DSO. We firstly select some well-matched static stereo points in the latest keyframe to improve the accuracy of inverse depth calculation for tracking. The inverse depth can further distinguish close objects from background, which will avoid large and far-away scene objects in keyframe determination. To boost efficiency and accuracy at the tracking stage, we propose a point selection method to sample map points and remove outliers. Furthermore, altitude smoothness verification with a local flat ground assumption and recovery method for tracking failure on bumpy roads are proposed to improve the system’s robustness. Finally, a far-away keyframe is reserved in the sliding window to alleviate the orientation drift since the agricultural robots usually move straightly following the crop row. Our system achieved new state-of-the-art results on Flourish dataset and the recently released Rosario dataset.