An Adaptive Strategy For Monocular Visual Odometry

Monocular Visual odometry is an important technique in mobile robot localization and navigation. This paper first empirically studies two kinds of commonly used monocular visual odometry (MVO): descriptor-based methods and optical flow based methods. Six representative scenes are extracted from KITTI and Karlsruhe datasets. Ten MVO algorithms are evaluated in terms of real-time performance and trajectory accuracy. Experimental results show that different MVO algorithms show different performance in different scenarios. Furthermore, an adaptive visual odometry(AVO) strategy is proposed on the basis of the experiment results. The changing environment is detected and the most suitable MVO algorithm is chosen dynamically according to a cost function. The experimental results show that the AVO method can obtain higher trajectory accuracy and better real-time performance.