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The localization with respect to a prior map is a fundamental requirement for mobile robots. The commonly used adaptive monte carlo localization (AMCL) can be found on most of the mobile robots ranging from small cleaning robots to large AGVs. While achieving accurate pose estimates in static environments, this algorithm tends to fail in the presence of significant changes. Recently published extensions...
Visual place recognition (VPR) in changing environments is an urgent challenge for long-term autonomous navigation. One recent ConvNet landmark-based approach exploits region landmarks coupled with ConvNet features to match images, and the approach has shown promising results under significant environmental and viewpoint changes. In this paper, we propose a robust ConvNet landmark-based system for...
Mapping evolving environments requires an update mechanism to efficiently deal with dynamic objects. In this context, we propose a new approach to update maps pertaining to large-scale dynamic environments with semantics. While previous works mainly rely on large amount of observations, the proposed framework is able to build a stable representation with only two observations of the environment. To...
Navigation in large scale environments is challenging because it requires accurate local map and global relocation ability. We present a new hybrid metric-topological-semantic map structure, called MTS-map, that allows a fine metric-based navigation and fast coarse query-based localisation. It consists of local sub-maps connected through two topological layers at metric and semantic levels. Semantic...
We report on recent advances in the project TAKE on the Scientist's Workbench, a platform demonstrating the use of natural language processing (NLP) to support scientists or other knowledge workers in fast and better access to digital document content. We focus on sentence-wise NLP analysis of electronically available scientific paper texts. With the generated NLP annotations, we are able to provide...
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