Re-localization for Self-Driving Cars using Semantic Maps

Lhilo Kenye, Rishitha Palugulla, Mehul Arora, Bharath Bhat, R. Kala, Abhijeet Nayak
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引用次数: 3

Abstract

Localization of vehicle in adverse conditions such as in dense traffic conditions is a challenging problem and state-of-the-art techniques often make the vehicle get lost, requiring a re-localization technique to correctly reset the vehicle pose. The visual place recognition and loop-closure based re-localization techniques need to store a very large map and take a lot of time to re-localize the vehicle. We solve the problem by making a semantic map which is used to re-localize the vehicle, if and once it gets lost by conventional localization techniques. The semantic map is created using a test vehicle with sophisticated sensors, and the map can be used by any vehicle with a stereo camera for re-localization. It is assumed that the test vehicle has a budget stereo camera which produces numerous false positives to be rejected by the re-localizer; while the vehicle also misses many key landmarks during the run due to heavy traffic. These are the challenges which are overcome by the designed re-localization algorithm. The vehicle is tested on a highway scenario in Bengaluru, India for multiple runs in a highway segment. Results confirm accurate re-localization on a semantic map generated from road-signs.
基于语义地图的自动驾驶汽车再定位
在拥挤的交通条件下,车辆的定位是一个具有挑战性的问题,最先进的技术经常使车辆迷路,需要重新定位技术来正确地重置车辆的姿态。基于视觉位置识别和闭环的再定位技术需要存储非常大的地图,并且需要花费大量的时间来重新定位车辆。我们通过制作语义地图来解决这个问题,该地图可以在传统定位技术丢失车辆时用于重新定位车辆。语义地图是使用带有复杂传感器的测试车辆创建的,该地图可以被任何带有立体摄像头的车辆用于重新定位。假设测试车辆有一个预算立体摄像机,该摄像机会产生许多误报,被重新定位器拒绝;同时,由于交通拥挤,车辆在运行过程中也错过了许多关键的地标。所设计的重新定位算法克服了这些挑战。该车在印度班加罗尔的高速公路上进行了多次测试。结果证实了从道路标志生成的语义地图上准确的重新定位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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