Direct Sparse Odometry

Direct Sparse Odometry (DSO) is a visual odometry technique aiming to efficiently and accurately estimate camera motion from image sequences by directly minimizing photometric errors between consecutive frames, using only a sparse set of keypoints. Current research focuses on improving DSO's robustness and speed through various strategies, including integrating it with other sensor modalities (e.g., inertial measurement units, depth cameras, event cameras), leveraging geometric constraints like planarity, and employing advanced optimization techniques like Gaussian splatting and genetic algorithms. These advancements are significant for applications requiring real-time 3D mapping and localization in robotics, autonomous driving, and augmented reality, particularly in challenging environments with varying lighting or dynamic motion.