Illumination Invariance Adaptive Sidewalk Detection Based on Unsupervised Feature Learning

Abstract

To solve the problem of road recognition when the robot is driving on the sidewalk, a novel sidewalk detection algorithm from the first-person perspective is proposed, which is crucial for robot navigation. The algorithm starts from the illumination invariance graph of the sidewalk image, and the sidewalk “seeds” are selected dynamically to get the sidewalk features for unsupervised feature learning. The final sidewalk region will be extracted by multi-threshold adaptive segmentation and connectivity processing. The key innovations of the proposed algorithm are the method of illumination invariance based on PCA and the unsupervised feature learning for sidewalk detection. With the PCA-based illumination invariance, it can calculate the lighting invariance angle dynamically to remove the impact of illumination and different brick colors’ influence on sidewalk detection. Then the sidewalk features are selected dynamically using the parallel geometric structure of the sidewalk, and the confidence region of the sidewalk is obtained through unsupervised feature learning. The proposed method can effectively suppress the effects of shadows and different colored bricks in the sidewalk area. The experimental result proves that the F-measure of the proposed algorithm can reach 93.11% and is at least 7.7% higher than the existing algorithm.