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.