A real-time visual odometry method based on crosscheck of feature

Odometry is widely applied for continuously obtaining system poses in automatic drive system and robot navigation system. Visual odometry can achieve high precision of target motion trajectory estimation with low cost, while feature-based visual odometry has the advantages of low time complexity and high processing speed which are conducive to real-time processing. However, traditional feature-based visual odometry has two technical bottlenecks:low accuracy of feature detection and matching, and the low effectiveness of objective function weight in pose estimation. To address the low accuracy for the feature matching between frames, we present the crosscheck feature matching strategy. It adds the reverse check on the foundation of traditional single-track 'circle' matching strategy to obtain more accurate matching feature sets. This strategy increases inlier ratio and solves the low robustness problem in a single-track 'circle' strategy, which improves estimation accuracy. Meanwhile, we use motion information of previous frame to reduce the searching scope of current frame in crosscheck strategy. To address the low effectiveness of objective function weight, we use the occurrence number of features as its life cycle and present a objective function weight setting method that adaptively considers the life cycle of extracted features. In pose estimation, the life cycle of feature can reflect the stability of features and the objective function weight based on it can decrease the accumulative error. We evaluate the proposed method on publicly available KITTI dataset. The experimental results demonstrate that the proposed method can achieve high-accuracy real-time visual odometry calculation.