Multi-vehicle dataset combines camera LiDAR radar and 3D scans for precise auto-annotation
A Multi-Vehicle Dataset with Camera, LiDAR, and Radar Sensors and Scanned 3D Models for Custom Auto-Annotation using RTK-GNSS
RoboticsArtificial IntelligenceComputer Vision and Pattern RecognitionMachine Learning
Summary
Accurate data is important for teaching self-driving cars to understand their surroundings. This work offers a new dataset that includes images, laser scans, radar data, and detailed 3D models of multiple cars, along with precise positioning information. This allows others to create highly accurate labels and test how sensors handle challenges like objects blocking each other or reflections. The dataset helps evaluate sensor measurements more deeply than previous collections. The authors also provide example uses to show how this data can be applied.
What this means in practice
- •For autonomous vehicle developers: Create customized vehicle annotations with precise 3D models and accurate pose data to improve perception algorithm testing.
- •For robotics engineers: Test how sensor measurement errors like occlusions and reflections affect robot navigation using multi-sensor data of dynamic environments.
Authors
Philipp Berthold, Bianca Forkel, Mirko Maehlisch
Abstract
Datasets are a crucial element in the development of perception algorithms. They relate sensor measurement data to annotated reference information and allow for the deduction of sensor and object characteristics. In autonomous driving, the reference data commonly consist of semantic image segmentation, point-wise associations, or bounding box annotations. The dataset proposed in this work, however, aims to dig deeper into the evaluation of measurement principles and provides scanned 3D models of all vehicles together with a pose and continuous kinematics reference obtained by RTK-GNSS. Combined, the state of the complete dynamic surrounding of the sensor vehicle is known for any point in time. Subsequent reference formats can be easily computed in user-defined granularity. This dataset involves single-object and multi-object recordings with seven target vehicles. In particular, measurement effects such as occlusion, as well as reflections, can be evaluated, as the normals of the shape of the target vehicles are known. We describe the dataset, discuss the technical background of its development, and briefly present exemplary evaluations.