It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation.A key insight explored in this work is that the loop-closing event inherently occurs sparsely, that is, the image currently being taken matches with only a small subset (if any) of previous images. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex @math -minimization problem. By leveraging fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation with no restriction imposed on how images should be represented, while requiring only that the representations are "close" to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using real-world datasets.
Some recent work has focused on loop closure under extreme changes in the environment such as different weather and or lighting conditions at different times of the day. For example, proposed the SeqSLAM that is able to localize with drastic lighting and weather changes by matching sequences of images with each other as opposed to single images. introduced the experience-based maps that learn the different appearances of the same place as it gradually changes in order to perform long-term localization. Building upon @cite_41 , also discovered new images to attain better localization. In addition, have explored geometric features such as lines for the task of loop closure detection in both indoor and outdoor scenarios. Note that if the information invariant to such changes can be extracted as in @cite_27 @cite_21 @cite_17 @cite_41 @cite_19 @cite_38 @cite_32 , the proposed formulation can also be used to obtain loop-closure hypotheses. Essentially, in this work we focus on finding loop closures given some discriminative descriptions such as descriptors and whole images, assuming no specific type of image representations.
@cite_38 @cite_41 @cite_21 @cite_32 @cite_19 @cite_27 @cite_17
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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. It is essential for a robot to be able to detect revisits or loop closures for long-term visual navigation. A key insight is that the loop-closing event inherently occurs sparsely, i.e., the image currently being taken matches with only a small subset (if any) of previous observations. Based on this observation, we formulate the problem of loop-closure detection as a sparse, convex 1-minimization problem. By leveraging on fast convex optimization techniques, we are able to efficiently find loop closures, thus enabling real-time robot navigation. This novel formulation requires no offline dictionary learning, as required by most existing approaches, and thus allows online incremental operation. Our approach ensures a global, unique hypothesis by choosing only a single globally optimal match when making a loop-closure decision. Furthermore, the proposed formulation enjoys a flexible representation, with no restriction imposed on how images should be represented, while requiring only that the representations be close to each other when the corresponding images are visually similar. The proposed algorithm is validated extensively using public real-world datasets. Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100 precision with recall rates of up to 60 . Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them. Compared to point landmarks, however, lines have strength in conveying the structural information of the environment. Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks. In this paper, we propose a place recognition algorithm using straight line features, which enables reliable loop closure detections in large complex environments under significant illumination changes. A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures. The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features. The experimental results show that the proposed method outperforms the others in challenging indoor environments.