End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles
Author:
Gu Junyi1ORCID, Lind Artjom2ORCID, Chhetri Tek Raj34ORCID, Bellone Mauro5ORCID, Sell Raivo1ORCID
Affiliation:
1. Department of Mechanical and Industrial Engineering, Tallinn University of Technology Tallinn, 12616 Tallinn, Estonia 2. Intelligent Transportation Systems Lab, Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia 3. Semantic Technology Institute (STI) Innsbruck, Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria 4. Center for Artificial Intelligence (AI) Research Nepal, Sundarharaincha 56604, Nepal 5. FinEst Centre for Smart Cities, Tallinn University of Technology, 19086 Tallinn, Estonia
Abstract
Autonomous driving vehicles rely on sensors for the robust perception of their surroundings. Such vehicles are equipped with multiple perceptive sensors with a high level of redundancy to ensure safety and reliability in any driving condition. However, multi-sensor, such as camera, LiDAR, and radar systems raise requirements related to sensor calibration and synchronization, which are the fundamental blocks of any autonomous system. On the other hand, sensor fusion and integration have become important aspects of autonomous driving research and directly determine the efficiency and accuracy of advanced functions such as object detection and path planning. Classical model-based estimation and data-driven models are two mainstream approaches to achieving such integration. Most recent research is shifting to the latter, showing high robustness in real-world applications but requiring large quantities of data to be collected, synchronized, and properly categorized. However, there are two major research gaps in existing works: (i) they lack fusion (and synchronization) of multi-sensors, camera, LiDAR and radar; and (ii) generic scalable, and user-friendly end-to-end implementation. To generalize the implementation of the multi-sensor perceptive system, we introduce an end-to-end generic sensor dataset collection framework that includes both hardware deploying solutions and sensor fusion algorithms. The framework prototype integrates a diverse set of sensors, such as camera, LiDAR, and radar. Furthermore, we present a universal toolbox to calibrate and synchronize three types of sensors based on their characteristics. The framework also includes the fusion algorithms, which utilize the merits of three sensors, namely, camera, LiDAR, and radar, and fuse their sensory information in a manner that is helpful for object detection and tracking research. The generality of this framework makes it applicable in any robotic or autonomous applications and suitable for quick and large-scale practical deployment.
Funder
European Union’s Horizon 2020 Research and Innovation Programme European Regional Development Fund
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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