Don't Look Back: Robustifying Place Categorization for Viewpoint- and Condition-Invariant Place Recognition

When a human drives a car along a road for the first time, they later recognize where they are on the return journey typically without needing to look in their rear-view mirror or turn around to look back, despite significant viewpoint and appearance change. Such navigation capabilities are typically attributed to our semantic visual understanding of the environment [1] beyond geometry to recognizing the types of places we are passing through such as "passing a shop on the left" or "moving through a forested area". Humans are in effect using place categorization [2] to perform specific place recognition even when the viewpoint is 180 degrees reversed. Recent advances in deep neural networks have enabled high-performance semantic understanding of visual places and scenes, opening up the possibility of emulating what humans do. In this work, we develop a novel methodology for using the semantics-aware higher-order layers of deep neural networks for recognizing specific places from within a reference database. To further improve the robustness to appearance change, we develop a descriptor normalization scheme that builds on the success of normalization schemes for pure appearance-based techniques such as SeqSLAM [3]. Using two different datasets - one road-based, one pedestrian-based, we evaluate the performance of the system in performing place recognition on reverse traversals of a route with a limited field of view camera and no turn-back-and-look behaviours, and compare to existing state-of-the-art techniques and vanilla off-the-shelf features. The results demonstrate significant improvements over the existing state of the art, especially for extreme perceptual challenges that involve both great viewpoint change and environmental appearance change. We also provide experimental analyses of the contributions of the various system components.Comment: 9 pages, 11 figures, ICRA 201

Specification of high-level application programming interfaces (SemSorGrid4Env)

This document defines an Application Tier for the SemsorGrid4Env project. Within the Application Tier we distinguish between Web Applications - which provide a User Interface atop a more traditional Service Oriented Architecture - and Mashups which are driven by a REST API and a Resource Oriented Architecture. A pragmatic boundary is set to enable initial development of Web Applications and Mashups; as the project progresses an evaluation and comparison of the two paradigms may lead to a reassessment of where each can be applied within the project, with the experience gained providing a basis for general guidelines and best practice. Both Web Applications and Mashups are designed and delivered through an iterative user-centric process; requirements generated by the project case studies are a key element of this approach