aUToTrack: 自動運転チャレンジ向け軽量物体検出・追跡システム
aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge
GPSで歩行者の位置と速度を正解ラベル付けした新データセットUofTPed50を構築し、視覚・LiDAR・GPS/IMUを統合してCPUのみでリアルタイムに歩行者を検出・追跡する軽量システムaUToTrackを提案した。
著者: Keenan Burnett, Sepehr Samavi, Steven L. Waslander, Timothy D. Barfoot, Angela P. Schoellig
分類: cs.RO
原文アブストラクト
The University of Toronto is one of eight teams competing in the SAE AutoDrive Challenge -- a competition to develop a self-driving car by 2020. After placing first at the Year 1 challenge, we are headed to MCity in June 2019 for the second challenge. There, we will interact with pedestrians, cyclists, and cars. For safe operation, it is critical to have an accurate estimate of the position of all objects surrounding the vehicle. The contributions of this work are twofold: First, we present a new object detection and tracking dataset (UofTPed50), which uses GPS to ground truth the position and velocity of a pedestrian. To our knowledge, a dataset of this type for pedestrians has not been shown in the literature before. Second, we present a lightweight object detection and tracking system (aUToTrack) that uses vision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance on the KITTI Object Tracking benchmark. We show that aUToTrack accurately estimates the position and velocity of pedestrians, in real-time, using CPUs only. aUToTrack has been tested in closed-loop experiments on a real self-driving car, and we demonstrate its performance on our dataset.
