utbm_robocar_dataset
A long-term autonomous driving dataset from Europe with multi-sensor data (GPS-RTK, LiDAR, cameras, IMU) for localization and mapping research.
What is utbm_robocar_dataset?
The UTBM Robocar Dataset is a long-term, multi-sensor dataset for autonomous driving research, collected from a vehicle equipped with GPS-RTK, LiDAR, cameras, and an IMU. It addresses the need for real-world, varied environmental data to develop and test algorithms for vehicle localization, mapping, and long-term autonomy.
Researchers and engineers in robotics and autonomous systems focusing on localization, SLAM, and perception, particularly those using ROS and working with sensor fusion.
It offers a unique long-term perspective with seasonal changes, provides high-precision GPS-RTK ground truth, and includes ready-to-use ROS baselines, enabling direct benchmarking and reducing setup time for experiments.
Overview
EU Long-term Dataset with Multiple Sensors for Autonomous Driving
Use Cases
Best For
- Benchmarking LiDAR odometry and SLAM algorithms like LOAM and LeGO-LOAM
- Researching long-term autonomy and localization under varying weather conditions
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