Visual navigation with efficient ConvNet features

Abstract

In this paper, we propose a system for autonomous vehicle following without a line of sight. From monocular camera images, the leading vehicle extracts scene descriptors which it transmits to the following vehicle by means of vehicle-to-vehicle (V2V) communication. The follower is able to recognize the scenes using its own camera and follow autonomously. A particle filter framework is employed for jump-free localization on the driven path of the leading vehicle. We compare the performance of different place features for accurate localization on a custom application-oriented dataset and evaluate methods to reduce the feature size for low-bandwidth V2V communication, while maintaining and even improving the recognition performance. Real-world results demonstrate the applicability of our system.