Condition-Invariant Multi-View Place Recognition
This work addresses the problem of robust place recognition for robotics and autonomous systems, though it appears incremental as it builds on existing deep network methods with temporal enhancements.
The paper tackled the challenge of visual place recognition under varying conditions like weather and time of day by leveraging deep networks and temporal sequence information, achieving more compact and better-performing descriptors than existing baselines on two public databases.
Visual place recognition is particularly challenging when places suffer changes in its appearance. Such changes are indeed common, e.g., due to weather, night/day or seasons. In this paper we leverage on recent research using deep networks, and explore how they can be improved by exploiting the temporal sequence information. Specifically, we propose 3 different alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep networks to use several frames of a sequence. We show that our approaches produce more compact and best performing descriptors than single- and multi-view baselines in the literature in two public databases.