Learning Scene Adaptive Covariance Error Model of LiDAR Scan Matching for Fusion Based Localization

Localization is an essential technique for many robotic tasks such as mapping and navigation. Scan matching has been fused with other sensors to solve the problem at GPS restricted areas, where an accurate error model describing matching precision at various scenes is indispensable. We proposed an end-to-end method to learn a scene adaptive error model of LiDAR scan matching. A CNN (Convolutional Neural Network) is learnt to map from a LiDAR scan to an information matrix of the matching result, and a localization framework is proposed to fuse the results of LiDAR scan matching based on its error model. Experiments are conducted using both simulated and real world data, where the former is to validate the proposed method of its adaptability at various simple but typical scenes, while the later is to examine the method's practicability at real world environments. We demonstrate the performance of learning covariance error model, and examine the localization accuracy by comparing with other traditional methods. Efficiency of the proposed method is demonstrated.