Simultaneous Localization and Mapping (SLAM) is a critical component of autonomous navigation, enabling mobile robots to construct maps while estimating their location. In this study, we compare the performance of SLAM Toolbox and Cartographer, two widely used 2D SLAM methods, by evaluating their ability to generate accurate maps for autonomous racing applications. The evaluation was conducted using real-world data collected from a RoboRacer vehicle equipped with a 2D laser scanner and capable of providing odometry, operating on a small test track. Both SLAM methods were tested offline. The resulting occupancy grid maps were analyzed using quantitative metrics and visualization tools to assess their quality and consistency. The evaluation was performed against ground truth data derived from an undistorted photograph of the racetrack.

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.

High-definition vector maps, such as Lanelet2, are critical for autonomous driving systems, enabling precise localization, path planning, and regulatory compliance. However, creating and maintaining these maps traditionally demands labor-intensive manual annotation or resource-heavy automated pipelines. This paper presents an ROS 2-based framework for semi-automatic vector map generation, leveraging Lanelet2 primitives to streamline map creation while balancing automation with human oversight. The framework integrates multi-sensor inputs (LIDAR, GPS/IMU) within ROS 2 to extract and fuse road features such as lanes, traffic signs, and curbs. The pipeline employs modular ROS 2 nodes for tasks including NDT and SLAM-based pose estimation and the semantic segmentation of drivable areas which serve as a basis for Lanelet2 primitives. To promote adoption, the implementation is released as an open source. This work bridges the gap between automated map generation and human expertise, advancing the practical deployment of dynamic vector maps in autonomous systems.