Evaluation of Long-term Deep Visual Place Recognition

Farid Alijani, Jukka Peltomäki, Jussi Puura, Heikki Huttunen, Joni-Kristian Kämäräinen, Esa Rahtu

Abstract: In this paper, we provide a comprehensive study on evaluating two state-of-the-art deep metric learning methods for visual place recognition. Visual place recognition is an essential component in the visual localization and the vision-based navigation where it provides an initial coarse location. It is used in variety of autonomous navigation technologies, including autonomous vehicles, drones and computer vision systems. We study recent visual place recognition and image retrieval methods and utilize them to conduct extensive and comprehensive experiments on two diverse and large long-term indoor and outdoor robot navigation datasets, e.g., COLD and Oxford Radar RobotCar along with ablation studies on the crucial parameters of the deep architectures. Our comprehensive results indicate that the methods can achieve 5 m of outdoor and 50 cm of indoor place recognition accuracy with high recall rate of 80 %.