Are State-of-the-art Visual Place Recognition Techniques any Good for Aerial Robotics?

Abstract

Visual Place Recognition (VPR) has seen significant advances at the frontiers of matching performance and computational superiority over the past few years. However, these evaluations are performed for ground-based mobile platforms and cannot be generalized to aerial platforms. The degree of viewpoint variation experienced by aerial robots is complex, with their processing power and on-board memory limited by payload size and battery ratings. Therefore, in this paper, we collect $8$ state-of-the-art VPR techniques that have been previously evaluated for ground-based platforms and compare them on $2$ recently proposed aerial place recognition datasets with three prime focuses: a) Matching performance b) Processing power consumption c) Projected memory requirements. This gives a birds-eye view of the applicability of contemporary VPR research to aerial robotics and lays down the the nature of challenges for aerial-VPR.

IEEE ICRA 2019 Workshop on Aerial Robotics 8 pages, 7 figures

References in corpus (3)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Convolutional Neural Network-based Place Recognition
• Levelling the Playing Field: A Comprehensive Comparison of Visual Place Recognition Approaches under Changing Conditions

Cited by in corpus (2)

• ConvSequential-SLAM: A Sequence-based, Training-less Visual Place Recognition Technique for Changing Environments
• Scene Retrieval for Contextual Visual Mapping