2020/11/01 by Mike Kasper, Kasper, Mike, Fernando Nobre +5 · 2 citations
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Consistency (knowledge bases) #Economics #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Mathematics #Metric (unit) #Operations management #Robotics (cs.RO) #Robotics and Sensor-Based Localization #cs.CV #cs.RO
paper · pdf · doi:10.48550/arxiv.2011.00608
published in arXiv (Cornell University) (Cornell University) · Accepted for publication in the 4th Conference on Robot Learning (CoRL 2020), Cambridge MA, USA
arxiv created 2020/11/01 · openalex publication_date 2020/11/01 · arxiv updated 2020/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Training networks to perform metric relocalization traditionally requires accurate image correspondences. In practice, these are obtained by restricting domain coverage, employing additional sensors, or capturing large multi-view datasets. We instead propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. Guided by this intuition, we derive a novel transform consistency loss. Using this loss function, we train a deep neural network to infer dense feature and saliency maps to perform robust metric relocalization in dynamic environments. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.