The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w.r.t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance of discriminative features within a bag-of-words (BOW) framework accompanied with approximate geometric verification. Though limiting the set of potential matches their performance crucially drops for increasing number of scans making them less appropriate for large scale environments. We present Geometrical Landmark Relations (GLARE), which transform 2D laser scans into pose invariant histogram representations. Potential matches are found in sub-linear time using an efficient Approximate Nearest Neighbour (ANN) search. Experimental results obtained from publicly available datasets demonstrate that GLARE significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings. Our Approach achieves recognition rates of 93% recall at 99% precision for a dataset covering a total path of about 6.5 km.

Automated guided vehicles require spatial representations of their working spaces in order to ensure safe navigation and carry out high-level tasks. Typically, these models are given by geometric maps. Even though these enable basic robotic navigation, they off-the-shelf lack the availability of task-dependent information required to provide services. This article presents a semantic mapping approach augmenting existing geometric representations. Our approach demonstrates the automatic annotation of map subspaces on the example of warehouse environments. The proposals of an object recognition system are integrated in a graph-based simultaneous localization and mapping framework and eventually propagated into a global map representation. Our system is experimentally evaluated in a typical warehouse consisting of common object classes expected for this type of environment. We discuss the novel achievements and motivate the contribution of semantic maps toward the operation of automated guided vehicles in the context of Industry 4.0.

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