Abstract
This paper addresses the problem of vehicle detection using a little explored LIDAR’s modality: the reflection intensity. LIDAR reflection measures the ratio of the received beam sent to a surface, which depends upon the distance, material, and the angle between surface normal and the ray. Considering a 3D-LIDAR mounted on board a robotic vehicle, which is calibrated with respect to a monocular camera, a Dense Reflection Map (DRM) is generated from the projected sparse LIDAR’s reflectance intensity, and inputted to a Deep Convolutional Neural Network (ConvNet) object detection framework for the vehicle detection. The performance on the KITTI is superior to some of the approaches that use LIDAR’s range-value, and hence it demonstrates the usability of LIDAR’s reflection for vehicle detection.
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Acknowledgments
This work has been supported by “AUTOCITS - Regulation Study for Interoperability in the Adoption of Autonomous Driving in European Urban Nodes” - Action number 2015-EU-TM-0243-S, co-financed by the European Union (INEA-CEF); and FEDER through COMPETE 2020, Portugal 2020 program under grant UID/EEA/00048/2013.
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Asvadi, A., Garrote, L., Premebida, C., Peixoto, P., Nunes, U.J. (2018). Real-Time Deep ConvNet-Based Vehicle Detection Using 3D-LIDAR Reflection Intensity Data. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds) ROBOT 2017: Third Iberian Robotics Conference. ROBOT 2017. Advances in Intelligent Systems and Computing, vol 694. Springer, Cham. https://doi.org/10.1007/978-3-319-70836-2_39
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DOI: https://doi.org/10.1007/978-3-319-70836-2_39
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