An adaptive network fusing light detection and ranging height-sliced bird’s-eye view and vision for place recognition

Abstract

Place recognition, a fundamental component of robotic perception, aims to identify previously visited locations within an environment. In this study, we present a novel global descriptor that uses height-sliced Bird’s Eye View (BEV) from Light Detection and Ranging (LiDAR) and vision images, to facilitate high-recall place recognition in autonomous driving field. Our descriptor generation network, incorporates an adaptive weights generation branch to learn weights of visual and LiDAR features, enhancing its adaptability to different environments. The generated descriptor exhibits excellent yaw-invariance. The entire network is trained using a self-designed quadruplet loss, which discriminates inter-class boundaries and alleviates overfitting to one particular modality. We evaluate our approach on three benchmarks derived from two public datasets and achieve optimal performance on these evaluation sets. Our approach demonstrates excellent generalization ability and efficient runtime, which are indicative of its practical viability in real-world scenarios. For those interested in applying this Artificial Intelligence contribution to engineering, the implementation of our approach can be found at: https://github.com/Bryan-ZhengRui/LocFuse.