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 this https URL

本文介绍了一种利用城市规模激光雷达地图的准确高效的视觉地点识别方法 VOLoc，该方法通过几何相似性在实时捕获的图像序列中直接查询压缩的激光雷达地图，实现了比激光雷达到激光雷达地点识别更好的定位精度。