Paper Info
Title
Denoising Autoencoders For Laser-Based Scan Registration
Abstract
In this letter, we build on recent advances in deep learning to improve SE(3) transformations, enabling more accurate motion estimation in mobile robots. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. Our proposed approach is comprised of two stages: a novel pre-processing stage for robust feature identification and a scan matching stage for motion estimation. In the pre-processing stage, LIDAR data are projected into a two-dimensional (2-D) image format and a DAE is used to extract salient features. These features are used as a mask for the original data, which is then re-projected into full 3-D space. Scan matching is performed on the re-projected data to estimate motion in SE(3). We analyze the performance of our approach using the real-world data from the University of Michigan North Campus long-term vision and LIDAR dataset and test generalization on LIDAR data from the KITTI dataset. We show that our approach generalizes across domains, is capable of reducing the per-estimate error of standard iterative closest point (ICP) methods on average by 25.5% for the translational component and 57.53% for the rotational component, and is capable of reducing the computation time of state-of-the-art ICP methods by a factor of 7.94 on average while achieving competitive performance.
Year
DOI
Venue
2018
10.1109/LRA.2018.2867856
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
Field
DocType
Autonomous vehicle navigation, learning and adaptive systems, localization
Computer vision,Robustness (computer science),Feature extraction,Control engineering,Image file formats,Lidar,Artificial intelligence,Motion estimation,Engineering,Deep learning,Mobile robot,Iterative closest point
Journal
Volume
Issue
ISSN
3
4
2377-3766
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Austin Nicolai152.24
Geoffrey A. Hollinger233427.61