Abstract
urban_road_filter is an open-source package built on a set of algorithms
presented across multiple papers. Road and sidewalk detection in urban scenarios is a
challenging task due to road surface imperfections and high sensor data bandwidth.
Traditional free-space and ground filter algorithms are not sensitive enough to detect
small height differences. Camera-based or sensor-fusion approaches are widely used to
classify drivable road from sidewalk or pavement, but a LIDAR sensor alone already
contains all the information needed for feature extraction. This work therefore focuses
on LIDAR-based feature extraction, presenting a real-time (20 Hz+) solution for road and
sidewalk detection that can also be used for local path planning. Sidewalk edge detection
is performed by combining three algorithms running in parallel. Results are validated
against the de facto standard benchmark dataset, KITTI, alongside our own measurements.
Data and source code to reproduce the results are shared publicly on our GitHub
repository.
Experimental results of the proposed method on a real-world urban environment, showing ther actual image and the detected road and sidewalk areas.
Explaination of the proposed method.
Experimental results of the proposed method on a real-world urban environment, showing the detected road and sidewalk areas.
BibTeX
If you use this work, please cite one of the following:@Article{roadfilt2022horv,
title = {Real-Time LIDAR-Based Urban Road and Sidewalk Detection for Autonomous Vehicles},
author = {Horváth, Ernő and Pozna, Claudiu and Unger, Miklós},
journal = {Sensors},
volume = {22},
year = {2022},
number = {1},
url = {https://www.mdpi.com/1424-8220/22/1/194},
issn = {1424-8220},
doi = {10.3390/s22010194}
}