L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving

Weixin Lu, Yao Zhou, Guowei Wan, Shenhua Hou, Shiyu Song; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 6389-6398

Abstract


We present L3-Net - a novel learning-based LiDAR localization system that achieves centimeter-level localization accuracy, comparable to prior state-of-the-art systems with hand-crafted pipelines. Rather than relying on these hand-crafted modules, we innovatively implement the use of various deep neural network structures to establish a learning-based approach. L3-Net learns local descriptors specifically optimized for matching in different real-world driving scenarios. 3D convolutions over a cost volume built in the solution space significantly boosts the localization accuracy. RNNs are demonstrated to be effective in modeling the vehicle's dynamics, yielding better temporal smoothness and accuracy. We comprehensively validate the effectiveness of our approach using freshly collected datasets. Multiple trials of repetitive data collection over the same road and areas make our dataset ideal for testing localization systems. The SunnyvaleBigLoop sequences, with a year's time interval between the collected mapping and testing data, made it quite challenging, but the low localization error of our method in these datasets demonstrates its maturity for real industrial implementation.

Related Material


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[bibtex]
@InProceedings{Lu_2019_CVPR,
author = {Lu, Weixin and Zhou, Yao and Wan, Guowei and Hou, Shenhua and Song, Shiyu},
title = {L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}