Vision based method for the localization of intelligent vehicles in loose constraint area

The localization is always an important research topic in the field of intelligent vehicle. This paper proposed a novel accurate localization method for intelligent vehicle navigation in loose constraint area (LCA) that uses only a single monocular camera. First, to eliminate the impact of the perspective effect and reduce the computational dimension, Harris corner feature points of the raw image are projected to the Inverse Perspective Image. Match them with feature point from the feature local map, using Normalized Cross-Correlation algorithm (NCC), calculate the optimal localization of vehicle using Random Sample Consensus algorithm (RANSAC) assisted Extended Kalman filter and then, update the feature local map. The proposed methodology is validated in the real world using an intelligent vehicle; it also has high position accuracy and robustness in the complex illumination.

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