Road and sidewalk detection in urban scenarios is a challenging task because of the road imperfections and high sensor data bandwidth. Traditional free space and ground filter algorithms are not sensitive enough for small height differences. Camera-based or sensor-fusion solutions are widely used to classify drivable road from sidewalk or pavement. A LIDAR sensor contains all the necessary information from which the feature extraction can be done. Therefore, this paper focuses on LIDAR-based feature extraction. For road and sidewalk detection, the current paper presents a real-time (20 Hz+) solution. This solution can also be used for local path planning. Sidewalk edge detection is the combination of three algorithms working parallelly. To validate the result, the de facto standard benchmark dataset, KITTI, was used alongside our measurements. The data and the source code to reproduce the results are shared publicly on our GitHub repository.

Autonomous navigation in urban environments demands robust real-time detection of drivable surfaces despite high-throughput LIDAR data. While majority of current approaches often rely on camera-based or multi-sensor fusion systems, this paper introduces an enhancement of our previous LIDAR-centric solution integrated within the Robot Operating System 2 (ROS 2) framework to address computational efficiency and precision challenges. We propose a parallelized algorithm suite for LIDAR-based road and sidewalk detection, achieving processing rates exceeding 20 Hz. Validation on the KITTI benchmark and own datasets demonstrates improved accuracy in complex urban scenarios compared to traditional ground-filtering techniques. To foster reproducibility, the ROS 2-compliant implementation, datasets, and evaluation scripts are publicly released. This work underscores the potential of LIDAR sensors coupled with modern robotic frameworks to enhance perception pipelines in autonomous systems.

As autonomous technologies flourish within the vehicle industry, an increasing number of academic autonomous competitions are appearing. One of them is the Shell Eco-marathon Autonomous Urban Concept competition (SEM AUC) which seeks to provide hands-on experience for the academic community to design, build and test their own driverless vehicles within a realistic infrastructure. The team at our university participates in this competition and our concept is to rely on simple and robust algorithms. This paper presents a simple collision-free space estimation algorithm for the LiDAR sensor.