Paper Info
Title
Real-Time Detection of Non-Stationary Objects Using Intensity Data in Automotive LiDAR SLAM
Abstract
This article aims at demonstrating the feasibility of modern deep learning techniques for the real-time detection of non-stationary objects in point clouds obtained from 3-D light detecting and ranging (LiDAR) sensors. The motion segmentation task is considered in the application context of automotive Simultaneous Localization and Mapping (SLAM), where we often need to distinguish between the static parts of the environment with respect to which we localize the vehicle, and non-stationary objects that should not be included in the map for localization. Non-stationary objects do not provide repeatable readouts, because they can be in motion, like vehicles and pedestrians, or because they do not have a rigid, stable surface, like trees and lawns. The proposed approach exploits images synthesized from the received intensity data yielded by the modern LiDARs along with the usual range measurements. We demonstrate that non-stationary objects can be detected using neural network models trained with 2-D grayscale images in the supervised or unsupervised training process. This concept makes it possible to alleviate the lack of large datasets of 3-D laser scans with point-wise annotations for non-stationary objects. The point clouds are filtered using the corresponding intensity images with labeled pixels. Finally, we demonstrate that the detection of non-stationary objects using our approach improves the localization results and map consistency in a laser-based SLAM system.
Year
DOI
Venue
2021
10.3390/s21206781
SENSORS
Keywords
DocType
Volume
3-D LiDAR, SLAM, intensity data, motion segmentation, deep learning
Journal
21
Issue
ISSN
Citations 
20
1424-8220
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Tomasz Nowak100.34
Krzysztof Cwian221.41
Piotr Skrzypczynski314825.07