Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable “duplicate” landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loopclosure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of “best” hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.
The map merging and data association in this paper touches many subfields of SLAM (and robotics in general). As a solution to the loop-closure and relocalization problem, it provides an alternative to direct image-based localization methods, such as @cite_1 @cite_21 @cite_20 . However, such systems require a rich database of places'' (i.e., keyframes) to localize against, and are sensitive to variations in viewpoint, lighting, or scene change. Recently, @cite_8 achieved good robustness to extreme viewpoint and appearance variation by leveraging semantic information (similar in spirit to our approach) via per-pixel semantic classification.
@cite_21 @cite_1 @cite_20 @cite_8
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This paper describes a probabilistic framework for appearance based navigation and mapping using spatial and visual appearance data. Like much recent work on appearance based navigation we adopt a bag-of-words approach in which positive or negative observations of visual words in a scene are used to discriminate between already visited and new places. In this paper we add an important extra dimension to the approach. We explicitly model the spatial distribution of visual words as a random graph in which nodes are visual words and edges are distributions over distances. Care is taken to ensure that the spatial model is able to capture the multi-modal distributions of inter-word spacing and account for sensor errors both in word detection and distances. Crucially, these inter-word distances are viewpoint invariant and collectively constitute strong place signatures and hence the impact of using both spatial and visual appearance is marked. We provide results illustrating a tremendous increase in precision-recall area compared to a state-of-the-art visual appearance only systems. Within the context of Simultaneous Localisation and Mapping (SLAM), “loop closing” is the task of deciding whether or not a vehicle has, after an excursion of arbitrary length, returned to a previously visited area. Reliable loop closing is both essential and hard. It is without doubt one of the greatest impediments to long term, robust SLAM. This paper illustrates how visual features, used in conjunction with scanning laser data, can be used to a great advantage. We use the notion of visual saliency to focus the selection of suitable (affine invariant) image-feature descriptors for storage in a database. When queried with a recently taken image the database returns the capture time of matching images. This time information is used to discover loop closing events. Crucially this is achieved independently of estimated map and vehicle location. We integrate the above technique into a SLAM algorithm using delayed vehicle states and scan matching to form interpose geometric constraints. We present initial results using this system to close loops (around 100m) in an indoor environment. Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. We present a relocalization module for such systems which solves some of the problems encountered by previous monocular SLAM systems-tracking failure, map merging, and loop closure detection. This module extends recent advances in keypoint recognition to determine the camera pose relative to the landmarks within a single frame time of 33 ms. We first show how this module can be used to improve the robustness of these systems. Blur, sudden motion, and occlusion can all cause tracking to fail, leading to a corrupted map. Using the relocalization module, the system can automatically detect and recover from tracking failure while preserving map integrity. Extensive tests show that the system can then reliably generate maps for long sequences even in the presence of frequent tracking failure. We then show that the relocalization module can be used to recognize overlap in maps, i.e., when the camera has returned to a previously mapped area. Having established an overlap, we determine the relative pose of the maps using trajectory alignment so that independent maps can be merged and loop closure events can be recognized. The system combining all of these abilities is able to map larger environments and for significantly longer periods than previous systems.