We propose a data structure obtained by hierarchically averaging bag-of-word descriptors during a sequence of views that achieves average speedups in large-scale loop closure applications ranging from 4 to 20 times on benchmark datasets. Although simple, the method works as well as sophisticated agglomerative schemes at a fraction of the cost with minimal loss of performance.
For scalability, the most common choice is to combine quantized local descriptors into a bag-of-words and then use an inverted index. FAB-MAP @cite_47 extends the basic setup by learning a generative model of the visual words using a Chow-Liu tree to model the probability of co-occurrence of visual words. This allows calling a loop closure even when very few common features are observed while rejecting false matches which might be similar in appearance but generated by different locations. FAB-MAP 2.0 @cite_25 scales further by exploiting sparsity to make the inverted index retrieval architecture more efficient. Angeli al @cite_1 adopt Recursive Bayesian filtering with a simple voting scheme based on term frequency-inverted document frequency (tf-idf) @cite_9 @cite_46 @cite_37 . Starting from @cite_4 , SIFT or SURF descriptors were replaced by more efficient binary descriptors such as BRIEF @cite_18 and ORB @cite_39 to achieve comparable precision and recall to FAB-MAP 2.0 with an order of magnitude speed increase. Several recent mapping and localization systems adopt it as a module, including @cite_23 and ORB-SLAM @cite_6 . These also further incorporate ideas from graph optimization @cite_10 @cite_26 .
@cite_47 @cite_37 @cite_4 @cite_18 @cite_26 @cite_9 @cite_1 @cite_6 @cite_39 @cite_23 @cite_46 @cite_10 @cite_25
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This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics. We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre-computed (using vector quantization), and inverted file systems and document rankings are used. The result is that retrieved is immediate, returning a ranked list of key frames shots in the manner of Google. The method is illustrated for matching in two full length feature films. We present a method for detecting revisited places in a image sequence in real time by using efficient features. We introduce three important novelties to the bag-of-words plus geometrical checking approach. We use FAST keypoints and BRIEF descriptors, which are binary and very fast to compute (less that 20µs per point). To perform image comparisons, we make use of a bag of words that discretises the binary descriptor space and an inverse index. We also introduce the use of a direct index to take advantage of the bag of words to obtain correspondence points between two images efficiently, avoiding a matching of complexity Θ(n2). To detect loop closure candidates, we propose managing matches in groups to increase the reliability of the candidates returned by the bag of words. We present results in three real and public datasets, with 0.7–1.7 Km long trajectories. We obtain high precision and recall rates, spending 16 ms on average per image for the feature computation and the whole loop detection process in sequences with 19000 images, one order of magnitude less than other similar techniques. We propose to use binary strings as an efficient feature point descriptor, which we call BRIEF. We show that it is highly discriminative even when using relatively few bits and can be computed using simple intensity difference tests. Furthermore, the descriptor similarity can be evaluated using the Hamming distance, which is very efficient to compute, instead of the L2 norm as is usually done. As a result, BRIEF is very fast both to build and to match. We compare it against SURF and U-SURF on standard benchmarks and show that it yields a similar or better recognition performance, while running in a fraction of the time required by either. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. In robotic applications of visual simultaneous localization and mapping techniques, loop-closure detection and global localization are two issues that require the capacity to recognize a previously visited place from current camera measurements. We present an online method that makes it possible to detect when an image comes from an already perceived scene using local shape and color information. Our approach extends the bag-of-words method used in image classification to incremental conditions and relies on Bayesian filtering to estimate loop-closure probability. We demonstrate the efficiency of our solution by real-time loop-closure detection under strong perceptual aliasing conditions in both indoor and outdoor image sequences taken with a handheld camera. This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor and outdoor environments. The system is robust to severe motion clutter, allows wide baseline loop closing and relocalization, and includes full automatic initialization. Building on excellent algorithms of recent years, we designed from scratch a novel system that uses the same features for all SLAM tasks: tracking, mapping, relocalization, and loop closing. A survival of the fittest strategy that selects the points and keyframes of the reconstruction leads to excellent robustness and generates a compact and trackable map that only grows if the scene content changes, allowing lifelong operation. We present an exhaustive evaluation in 27 sequences from the most popular datasets. ORB-SLAM achieves unprecedented performance with respect to other state-of-the-art monocular SLAM approaches. For the benefit of the community, we make the source code public. This paper presents a mathematical definition of the "probability-weighted amount of information" (PWI), a measure of specificity of terms in documents that is based on an information-theoretic view of retrieval events. The proposed PWI is expressed as a product of the occurrence probabilities of terms and their amounts of information, and corresponds well with the conventional term frequency-inverse document frequency measures that are commonly used in today's information retrieval systems. The mathematical definition of the PWI is shown, together with some illustrative examples of the calculation.