Visual Odometry in HDR Environments by Using Spatially Varying Exposure Camera

The accuracy and robustness of visual odometry (VO) is significantly affected by the high dynamic range (HDR) environments, because traditional cameras have a limited dynamic range and inevitably miss information in both overexposed and underexposed areas. To overcome the above challenge, we use an spatially varying exposure (SVE) camera, which captures four images with different exposure levels simultaneously. Then, we propose a VO pipeline that leverages the advantages of the SVE camera. Specifically, we extract ORB features from four images in parallel firstly instead of fusing four images, then perform merging and filtering to provide more robust features. We demonstrate that the proposed system outperforms comparable state-of-the-art methods in terms of robustness and accuracy. The real-time performance of the proposed system is also guaranteed due to the elaborate design of the parallel algorithm.