Scalable Place Recognition Under Appearance Change for Autonomous Driving
This addresses the problem of scalable place recognition for autonomous vehicles under environmental variations, but it appears incremental as it builds on existing methods with a focus on efficiency.
The paper tackles the challenge of robust place recognition for autonomous driving under appearance changes by proposing a scalable technique that efficiently retrains and compresses data, enabling recognition to exploit all available data without increasing computational cost. Experiments show it has greater potential for large-scale applications compared to state-of-the-art methods.
A major challenge in place recognition for autonomous driving is to be robust against appearance changes due to short-term (e.g., weather, lighting) and long-term (seasons, vegetation growth, etc.) environmental variations. A promising solution is to continuously accumulate images to maintain an adequate sample of the conditions and incorporate new changes into the place recognition decision. However, this demands a place recognition technique that is scalable on an ever growing dataset. To this end, we propose a novel place recognition technique that can be efficiently retrained and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from visible growth in computational cost. Underpinning our method is a novel temporal image matching technique based on Hidden Markov Models. Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.