Mutual Information Based Feature Selection for Stereo Visual Odometry

Visual odometry (VO) is one of the promising techniques that estimates pose using the camera and does not necessarily require other sensor aiding. With increasing automation and the use of miniaturized systems such as mobile devices, wearable gadgets, & gaming consoles, demand for efficient algorithms have risen. In this paper, an attempt is made to remove the redundant features from the VO pipeline that do not have a significant effect on the estimation process. A probabilistic approach based on fast mutual information (MI) computation is suggested here as the basis for removing features. The MI value acts as a beacon for selecting distinct features while eliminating the redundant ones, thus improving the overall system speed and reducing storage requirements. The proposed MI-based feature selection framework for VO has been experimented on the KITTI vision benchmark suite and EuRoC MAV datasets available publicly. The estimated trajectory results have shown that the proposed technique is better in terms of computational efficiency and has similar accuracy as compared to the normal VO pipeline. Further investigations have also been carried out over the VSLAM framework to test its applicability in a real-time system.