  Loop Closure Detection (LCD) is the essential module in the simultaneous
localization and mapping (SLAM) task. In the current appearance-based SLAM
methods, the visual inputs are usually affected by illumination, appearance and
viewpoints changes. Comparing to the visual inputs, with the active property,
light detection and ranging (LiDAR) based point-cloud inputs are invariant to
the illumination and appearance changes. In this paper, we extract 3D voxel
maps and 2D top view maps from LiDAR inputs, and the former could capture the
local geometry into a simplified 3D voxel format, the later could capture the
local road structure into a 2D image format. However, the most challenge
problem is to obtain efficient features from 3D and 2D maps to against the
viewpoints difference. In this paper, we proposed a synchronous adversarial
feature learning method for the LCD task, which could learn the higher level
abstract features from different domains without any label data. To the best of
our knowledge, this work is the first to extract multi-domain adversarial
features for the LCD task in real time. To investigate the performance, we test
the proposed method on the KITTI odometry dataset. The extensive experiments
results show that, the proposed method could largely improve LCD accuracy even
under huge viewpoints differences.
