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Stable feature extraction is the key for the Loop closure detection (LCD)task in the simultaneously localization and mapping (SLAM) framework. In ourpaper, the feature extraction is operated by using a generative adversarialnetworks (GANs) based unsupervised learning. GANs are powerful generativemodels, however, GANs based adversarial learning suffers from traininginstability. We find that the data-code joint distribution in the adversariallearning is a more complex manifold than in the original GANs. And the lossfunction that drive the attractive force between synthesis and targetdistributions is unable for efficient latent code learning for LCD task. Torelieve this problem, we combines the original adversarial learning with aninner cycle restriction module and a side updating module. To our bestknowledge, we are the first to extract the adversarial features from the lightdetection and ranging (LiDAR) based inputs, which is invariant to the changescaused by illumination and appearance as in the visual inputs. We use the KITTIodometry datasets to investigate the performance of our method. The extensiveexperiments results shows that, with the same LiDAR projection maps, theproposed features are more stable in training, and could significantly improvethe robustness on viewpoints differences than other state-of-art methods.

Towards Stable Adversarial Feature Learning for LiDAR based Loop Closure Detection