TY - GEN
T1 - Robust Place Recognition using an Imaging Lidar
AU - Shan, Tixiao
AU - Englot, Brendan
AU - Duarte, Fábio
AU - Ratti, Carlo
AU - Rus, Daniela
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85125484071&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125484071&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9562105
DO - 10.1109/ICRA48506.2021.9562105
M3 - Conference contribution
AN - SCOPUS:85125484071
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 5469
EP - 5475
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -