A Novel Loop Closure Detection Method Using Line Features

Abstract

Loop closure detection is a significant requirement for simultaneous localization and mapping (SLAM) to recognize revisited place. This paper presents a novel line-based loop closure detection method for vision-based SLAM that allows reliable loop closure detections, especial under structural environment. The performance of coping with perceptual aliasing conditions is more competitive than point based methods. The bag of words model is extended in this work which uses only line features. A variant of TF-IDF (term frequency & inverse document frequency) scoring scheme is proposed by adding a discrimination coefficient to improve the discrimination of image similarity scores, further to reinforce the similarity evaluation of two images. LBD (Line Band Descriptor) and binary LBD features are extracted to build visual vocabularies. Temporal consistency and spatial continuity checks enhance detection reliability. The performance of proposed scoring scheme was compared with original TF-IDF, results show that our proposed scheme has competitive discrimination ability. We also compared the query performance of our vocabularies with ORB-based, MSLD (mean standard-deviation line descriptor)-based, and PL (Point-and-Line)-based vocabularies, results indicate that our vocabularies obtain the highest successful retrieval rate. The performance of the whole loop closure detection algorithm was also evaluated in terms of precision, recall and efficiency, which were compared with ORB, MSLD, PL-based methods, and also with CNN-based method, results demonstrate that our method is superior to others with satisfactory precision and efficiency.

Keywords

• Vision-based SLAM
• bag of words
• binary LBD
• LBD
• a variant of TF-IDF scoring scheme