  We present a novel dataset named as HPointLoc, specially designed for
exploring capabilities of visual place recognition in indoor environment and
loop detection in simultaneous localization and mapping. The loop detection
sub-task is especially relevant when a robot with an on-board RGB-D camera can
drive past the same place (``Point") at different angles. The dataset is based
on the popular Habitat simulator, in which it is possible to generate
photorealistic indoor scenes using both own sensor data and open datasets, such
as Matterport3D. To study the main stages of solving the place recognition
problem on the HPointLoc dataset, we proposed a new modular approach named as
PNTR. It first performs an image retrieval with the Patch-NetVLAD method, then
extracts keypoints and matches them using R2D2, LoFTR or SuperPoint with
SuperGlue, and finally performs a camera pose optimization step with TEASER++.
Such a solution to the place recognition problem has not been previously
studied in existing publications. The PNTR approach has shown the best quality
metrics on the HPointLoc dataset and has a high potential for real use in
localization systems for unmanned vehicles. The proposed dataset and framework
are publicly available: https://github.com/metra4ok/HPointLoc.
