A novel cognition-inspired, agnostic framework is proposed for building maps in mobile robotics that are efficient in terms of image matching retrieval for solving Visual Place Recognition (VPR) problem. A dataset, 'ESSEX3IN1', is also presented to demonstrate the significantly enhanced performance of state-of-the-art VPR techniques when combined with the proposed framework.
Different research works have tried overcoming these intrinsic limitations of frame-cum-Place sampling by introducing Visual-distinctiveness based selection. Authors in @cite_6 use a custom-designed algorithm that detects change-point for segmentation between different topological places in both indoor and outdoor scenes. Image-sequence Partitioning for creating sparse topological maps is presented in @cite_13 , where sequences of images are partitioned into nodes places using four descriptors namely GIST, Optical Flow, Local Feature Mapping and Common-Important Words. In @cite_24 , a thematic approach is adapted to evaluate the novelty of an incoming image by co-relating it with the redundancy of visual features topics. Bayesian Surprise is adapted with immunity to sensor type, for extracting landmarks to create a sparse topological map in @cite_8 . Online Topic Modelling with visual surprise calculation is done in @cite_12 for under-water robotic explorations. An incremental unsupervised Place discovery scheme is adopted in @cite_9 which fuses information over time to find visually distinct places.
@cite_8 @cite_9 @cite_6 @cite_24 @cite_13 @cite_12
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Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. Our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. We evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector. This paper describes an online place discovery and recognition engine that fuses information over time to create topologically distinct places. A key motivation is the recognition that a single image may be a poor exemplar of what constitutes a place. Images are not ‘places’ nor are they ‘documents’. Instead, by treating image-sequences as a multimodal distribution over topics – and by discovering topics incrementally and online – it is possible to both reduce the memory footprint of place recognition systems, and to improve precision and recall. Distinctive key-places are represented by a cluster topics found from the covisibility graph of a relative simultaneous localization and mapping engine – key-places inherently span many images. A dynamic vocabulary of visual words and density based clustering is used to continually estimate a set of visual topics, changes in which drive the placerecognition process. The system is evaluated using an indoor robot sequence, a standard outdoor robot sequence and a longterm sequence from a static camera. Experiments demonstrate qualitatively distinct themes associated with discovered places – from common place types such as ‘hallway’, or ‘desk-area’, to temporal concepts such as ‘dusk’, ‘dawn’ or ‘mid-day’. Compared to traditional image-based place-recognition, this reduces the information that must be stored without reducing place-recognition performance. Topological navigation consists for a robot in navigating in a topological graph which nodes are topological places. Either for indoor or outdoor environments, segmentation into topological places is a challenging issue. In this paper, we propose a common approach for indoor and outdoor environment segmentation without elaborating a complete topological navigation system. The approach is novel in that environment sensing is performed using spherical images. Environment structure estimation is performed by a global structure descriptor specially adapted to the spherical representation. This descriptor is processed by a custom designed algorithm which detects change-points defining the segmentation between topological places. This paper is a demonstration of how a robot can, through introspection and then targeted data retrieval, improve its own performance. It is a step in the direction of lifelong learning and adaptation and is motivated by the desire to build robots that have plastic competencies which are not baked in. They should react to and benefit from use. We consider a particular instantiation of this problem in the context of place recognition. Based on a topic based probabilistic model of images, we use a measure of perplexity to evaluate how well a working set of background images explain the robot's online view of the world. Offline, the robot then searches an external resource to seek out additional background images that bolster its ability to localise in its environment when used next. In this way the robot adapts and improves performance through use. Most of the existing appearance based topological mapping algorithms produce dense topological maps in which each image stands as a node in the topological graph. Sparser maps can be built by representing groups of visually similar images as nodes of a topological graph. In this paper, we present a sparse topological mapping framework which uses Image Sequence Partitioning (ISP) techniques to group visually similar images as topological graph nodes. We present four different ISP techniques and evaluate their performance. In order to take advantage of the afore mentioned maps, we make use of Hierarchical Inverted Files (HIF) which enable efficient hierarchical loop closure. Outdoor experimental results demonstrating the sparsity, efficiency and accuracy achieved by the combination of ISP and HIF in performing loop closure are presented. Given an image stream, our on-line algorithm will select the semantically-important images that summarize the visual experience of a mobile robot. Our approach consists of data pre-clustering using coresets followed by a graph based incremental clustering procedure using a topic based image representation. A coreset for an image stream is a set of representative images that semantically compresses the data corpus, in the sense that every frame has a similar representative image in the coreset. We prove that our algorithm e ciently computes the smallest possible coreset under natural well-defined similarity metric and up to provably small approximation factor. The output visual summary is computed via a hierarchical tree of coresets for di↵erent parts of the image stream. This allows multi-resolution summarization (or a video summary of specified duration) in the batch setting and a memory-e cient incremental summary for the streaming case.