Real Time Monocular Visual Odometry Using Hybrid Features and Distance Ratio for Scale Estimation

Real time dead reckoning navigation is important for supplying information of the current position of an autonomous mobile robot to complete its task, especially in certain areas such as hazardous and GPS-denied areas. Monocular visual odometry is a good choice as it is one of the dead reckoning navigation method which only uses single camera. For real time task, visual odometry requires fast feature extraction without ignoring its accuracy. Therefore, we propose the usage of a hybrid feature, i.e. Censure feature detector and upright SURF feature descriptor, as feature extraction. Yet, the scale ambiguity for the monocular visual odometry becomes a challenging problem. Without additional information from other sensors, estimating the scale is solely the only way. In our proposed work, distance ratio is employed to tackle such problems. Experimental results show the performance of the designed algorithm. A real example of running the proposed algorithm under an embedded device is also provided for demonstrating its real time capability