Moving on to Dynamic Environments: Visual Odometry using Feature Classification

Visually estimating a robot's own motion has been an active field of research within the last years. Though impressive results have been reported, some application areas still exhibit huge challenges. Especially for car-like robots in urban environments even the most robust estimation techniques fail due to a vast portion of independently moving objects. Hence, we move one step further and propose a method that combines ego-motion estimation with low-level object detection. We specifically design the method to be general and applicable in real-time. Pre-classifying interest points is a key step, which rejects matches on possibly moving objects and reduces the computational load of further steps. Employing an Iterated Sigma Point Kalman Filter in combination with a RANSAC based outlier rejection scheme yields a robust frame-to-frame motion estimation even in the case when many independently moving objects cover the image. Extensive experiments show the robustness of the proposed approach in highly dynamic environments with speeds up to 20m/s.