イメージングLiDARによるロバストな場所認識
Robust Place Recognition using an Imaging Lidar
イメージングLiDARの強度画像からORB特徴とBoW表現を構築し、DBoWで高速検索した候補を特徴マッチングとRANSAC付きPnPで検証する、回転不変で逆走・上下反転再訪にも対応する場所認識手法を提案した。
著者: Tixiao Shan, Brendan Englot, Fabio Duarte, Carlo Ratti, Daniela Rus
分類: cs.CV, cs.RO
原文アブストラクト
We propose a methodology for robust, real-time place recognition using an imaging lidar, which yields image-quality high-resolution 3D point clouds. Utilizing the intensity readings of an imaging lidar, we project the point cloud and obtain an intensity image. ORB feature descriptors are extracted from the image and encoded into a bag-of-words vector. The vector, used to identify the point cloud, is inserted into a database that is maintained by DBoW for fast place recognition queries. The returned candidate is further validated by matching visual feature descriptors. To reject matching outliers, we apply PnP, which minimizes the reprojection error of visual features' positions in Euclidean space with their correspondences in 2D image space, using RANSAC. Combining the advantages from both camera and lidar-based place recognition approaches, our method is truly rotation-invariant, and can tackle reverse revisiting and upside down revisiting. The proposed method is evaluated on datasets gathered from a variety of platforms over different scales and environments. Our implementation and datasets are available at https://git.io/image-lidar
