VOLoc: Visual Place Recognition by Querying Compressed Lidar Map
Xudong Cai Yongcai Wang Zhe Huang Yu Shao Deying Li
The availability of city-scale Lidar maps enables the potential of city-scale
place recognition using mobile cameras. However, the city-scale Lidar maps
generally need to be compressed for storage efficiency, which increases the
difficulty of direct visual place recognition in compressed Lidar maps. This
paper proposes VOLoc, an accurate and efficient visual place recognition method
that exploits geometric similarity to directly query the compressed Lidar map
via the real-time captured image sequence. In the offline phase, VOLoc
compresses the Lidar maps using a \emph{Geometry-Preserving Compressor} (GPC),
in which the compression is reversible, a crucial requirement for the
downstream 6DoF pose estimation. In the online phase, VOLoc proposes an online
Geometric Recovery Module (GRM), which is composed of online Visual Odometry
(VO) and a point cloud optimization module, such that the local scene structure
around the camera is online recovered to build the \emph{Querying Point Cloud}
(QPC). Then the QPC is compressed by the same GPC, and is aggregated into a
global descriptor by an attention-based aggregation module, to query the
compressed Lidar map in the vector space. A transfer learning mechanism is also
proposed to improve the accuracy and the generality of the aggregation network.
Extensive evaluations show that VOLoc provides localization accuracy even
better than the Lidar-to-Lidar place recognition, setting up a new record for
utilizing the compressed Lidar map by low-end mobile cameras. The code are
publicly available at https://github.com/Master-cai/VOLoc.