Object segmentation and recognition in 3D point cloud with language model
we proposed a recognition algorithm based on feature extraction and Bag of words with language model. Object recognition in 3D rang data of big scene has become an increasingly popular research topic in intelligent vehicle area. Firstly we introduce a fast ground filtering and clustering method and a convenient method to generation training samples. Then we proposed a feature extraction method based on 3D SIFT and FPFH to get descriptors with scale invariant and rotationinvariant in 3D range data. Furthermore, we proposed a recognition algorithm based on bag of words and language model by add spatial semantic information to bag of words model to make up the shortcoming of ignoring the relationships between the visual words. At last, experimental results on real laser data depicting rural scenes are presented. Keywords point cloud segmentation; feature extraction; object recognition; 3D sift
MFI