Inertia constrained visual odometry for navigational applications

Motion estimation of a moving vehicle is one of the most important aspects of an autonomous navigation system. With increasing demand of navigation in GPS denied environments, vision-based navigation proves to be a promising area for applications varying from inter-terrestrial to small form factor mobile robotics. In this work, an inertia constrained visual odometry technique has been developed that helps in extracting rotation and translation information over consequent image frames. The inertia is considered here to be a bounding factor that restricts the position of the feature point in the incoming image frame. This constraint is found to reduce the overall outlier percentage and increase the estimation accuracy as compared to the traditional visual odometry pipeline. The proposed scheme is applied on the KITTI dataset logged for stereo camera and is compared with their ground truth poses.