  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
