Low resolution lidar-based multi object tracking for driving applications

Cita com:
hdl:2117/113342
Document typeConference report
Defense date2017
PublisherSpringer
Rights accessOpen Access
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Abstract
Vehicle detection and tracking in real scenarios are key com- ponents to develop assisted and autonomous driving systems. Lidar sen- sors are specially suitable for this task, as they bring robustness to harsh weather conditions while providing accurate spatial information. How- ever, the resolution provided by point cloud data is very scarce in com- parison to camera images. In this work we explore the possibilities of Deep Learning (DL) methodologies applied to low resolution 3D lidar sensors such as the Velodyne VLP-16 (PUCK), in the context of vehicle detection and tracking. For this purpose we developed a lidar-based sys- tem that uses a Convolutional Neural Network (CNN), to perform point- wise vehicle detection using PUCK data, and Multi-Hypothesis Extended Kalman Filters (MH-EKF), to estimate the actual position and veloci- ties of the detected vehicles. Comparative studies between the proposed lower resolution (VLP-16) tracking system and a high-end system, using Velodyne HDL-64, were carried out on the Kitti Tracking Benchmark dataset. Moreover, to analyze the influence of the CNN-based vehicle detection approach, comparisons were also performed with respect to the geometric-only detector. The results demonstrate that the proposed low resolution Deep Learning architecture is able to successfully accom- plish the vehicle detection task, outperforming the geometric baseline approach. Moreover, it has been observed that our system achieves a similar tracking performance to the high-end HDL-64 sensor at close range. On the other hand, at long range, detection is limited to half the distance of the higher-end sensor.
Description
The final publication is available at link.springer.com
Citationdel Pino, I., Vaquero, V., Massini, B., Solá, J., Moreno-Noguer, F., Sanfeliu, A., Andrade-Cetto, J. Low resolution lidar-based multi object tracking for driving applications. A: Iberian Robotics Conference. "ROBOT 2017 : Third Iberian Robotics Conference, vol. 1". Sevilla: Springer, 2017, p. 287-298.
ISBN978-3-319-70832-4
Publisher versionhttps://link.springer.com/chapter/10.1007%2F978-3-319-70833-1_24
Collections
- IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Ponències/Comunicacions de congressos [463]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.451]
- VIS - Visió Artificial i Sistemes Intel·ligents - Ponències/Comunicacions de congressos [287]
- ROBiri - Grup de Robòtica de l'IRI - Ponències/Comunicacions de congressos [219]
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