Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition

Published in IEEE IROS, 2018

Recommended citation: Peng Yin, Lingyun Xu, Zhe Liu, Lu Li, Hadi Salman, Yuqing He. (2018). " Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition " IEEE IROS. http://maxtomCMU.github.io/files/08593562.pdf

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Abstract: Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot’s motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What’s more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.

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