  Stable feature extraction is the key for the Loop closure detection (LCD)
task in the simultaneously localization and mapping (SLAM) framework. In our
paper, the feature extraction is operated by using a generative adversarial
networks (GANs) based unsupervised learning. GANs are powerful generative
models, however, GANs based adversarial learning suffers from training
instability. We find that the data-code joint distribution in the adversarial
learning is a more complex manifold than in the original GANs. And the loss
function that drive the attractive force between synthesis and target
distributions is unable for efficient latent code learning for LCD task. To
relieve this problem, we combines the original adversarial learning with an
inner cycle restriction module and a side updating module. To our best
knowledge, we are the first to extract the adversarial features from the light
detection and ranging (LiDAR) based inputs, which is invariant to the changes
caused by illumination and appearance as in the visual inputs. We use the KITTI
odometry datasets to investigate the performance of our method. The extensive
experiments results shows that, with the same LiDAR projection maps, the
proposed features are more stable in training, and could significantly improve
the robustness on viewpoints differences than other state-of-art methods.
