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by Mark Cummins
2008, The International Journal of Robotics …
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.
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This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25ms by the end of the trajectory.
2007, Robotics and Automation, 2007 …
This paper describes a probabilistic framework for navigation using only appearance data. By learning a generative model of appearance, we can compute not only the similarity of two observations, but also the probability that they originate from the same location, and hence compute a pdf over observer location. We do not limit ourselves to the kidnapped robot problem (localizing in a known map), but admit the possibility that observations may come from previously unvisited places. The principled probabilistic approach we develop allows us to explicitly account for the perceptual aliasing in the environment – identical but indistinctive observations receive a low probability of having come from the same place. Our algorithm complexity is linear in the number of places, and is particularly suitable for online loop closure detection in mobile robotics.
2009, … Journal of Robotics …
In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full sixdegree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association
2008, International Journal of Robotic Research
2012, 2012 IEEE International Conference on Robotics and Automation
We outline an approach for using concentration inequalities to perform rapid approximate multi-hypothesis testing. In a scenario where multiple hypotheses are ranked according to a large set of features, our scheme improves the efficiency of selecting the best hypothesis by providing a “bail-out threshold” at which unpromising hypotheses can be excluded from further evaluation. We show how concentration inequalities can be used to derive principled bail-out thresholds, subject to a user-specified error tolerance. The technique is similar to the sequential probability ratio test, but is applicable in more general conditions. We apply the technique to improve the speed of the fast-appearance-based mapping system for appearance-based place recognition and mapping. The speed increase provided by the new approach is data dependent, but we demonstrate speed improvements of between 25x − 50x on real data, with only a slight degradation in accuracy.
2014, Robotica
2011, Robotics Research
2008, Robotics and automation, 2008. …
This paper describes a probabilistic bail-out condition for multihypothesis testing based on Bennett’s inequality. We investigate the use of the test for increasing the speed of an appearance-only SLAM system where locations are recognised on the basis of their sensory appearance. The bail-out condition yields speed increases between 25x-50x on real data, with only slight degradation in accuracy. We demonstrate the system performing real-time loop closure detection on a mobile robot over multiple-kilometre paths in initially unknown outdoor environment
2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)
The most common way to deal with the uncertainty present in noisy sensorial perception and action is to model the problem with a probabilistic framework. Maximum likelihood estimation is a well-known estimation method used in many robotic and computer vision applications. Under Gaussian assumption, the maximum likelihood estimation converts to a nonlinear least squares problem. Efficient solutions to nonlinear least squares exist and they are based on iteratively solving sparse linear systems until convergence. In general, the existing solutions provide only an estimation of the mean state vector, the resulting covariance being computationally too expensive to recover. Nevertheless, in many simultaneous localization and mapping (SLAM) applications, knowing only the mean vector is not enough. Data association, obtaining reduced state representations, active decisions and next best view are only a few of the applications that require fast state covariance recovery. Furthermore, computer vision and robotic applications are in general performed online. In this case, the state is updated and recomputed every step and its size is continuously growing, therefore, the estimation process may become highly computationally demanding. This paper introduces a general framework for incremental maximum likelihood estimation called SLAM++, which fully benefits from the incremental nature of the online applications, and provides efficient estimation of both the mean and the covariance of the estimate. Based on that, we propose a strategy for maintaining a sparse and scalable state representation for large scale mapping, which uses information theory measures to integrate only informative and non-redundant contributions to the state representation. SLAM++ differs from existing implementations by performing all the matrix operations by blocks. This led to extremely fast matrix manipulation and arithmetic operations used in nonlinear least squares. Even though this paper tests SLAM++ efficiency on SLAM problems, its applicability remains general.
2009, Autonomous Robots
2012, Robotics: Science and Systems VIII
2011
Abstract We present a novel algorithm for topological mapping, which is the problem of finding the graph structure of an environment from a sequence of measurements. Our algorithm, called Online Probabilistic Topological Mapping (OPTM), systematically addresses the problem by constructing the posterior on the space of all possible topologies given measurements. With each successive measurement, the posterior is updated incrementally using a Rao—Blackwellized particle filter.
2012
Abstract We propose a place recognition algorithm for simultaneous localization and mapping (SLAM) systems using stereo cameras that considers both appearance and geometric information of points of interest in the images. Both near and far scene points provide information for the recognition process. Hypotheses about loop closings are generated using a fast appearance-only technique based on the bag-of-words (BoW) method.
2009, Computer Vision and …
2007, Advanced Robotics
2010
2000, IEEE Transactions on Robotics
2008, IEEE Transactions on Robotics
2007, The International Journal of Robotics Research
, journal = {{IEEE Transactions on Robotics}}, year = {2016}, number = {6}, pages = {1309–1332}, volume = {32} } Abstract—Simultaneous Localization And Mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?
2009
2010, Computer Science and Artificial Intelligence Laboratory. MIT technical report MIT-CSAIL-TR-2010-021
In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to ...
2008, IEEE Transactions on Robotics
2009, Proceedings of the IEEE International Conference …
2010
Place recognition is a challenging task in any SLAM system. Algorithms based on visual appearance are becoming popular to detect locations already visited, also known as loop closures, because cameras are easily available and provide rich scene detail. These algorithms typically result in pairs of images considered depicting the same location. To avoid mismatches, most of them rely on epipolar geometry to check spatial consistency. In this paper we present an alternative system that makes use of stereo vision and combines two complementary techniques: bag-of-words to detect loop closing candidate images, and conditional random fields to discard those which are not geometrically consistent. We evaluate this system in public indoor and outdoor datasets from the Rawseeds project, with hundred-metre long trajectories. Our system achieves more robust results than using spatial consistency based on epipolar geometry.
2008
Having a map of the environment is almost an essential pre-requisite for robots to perform any task requiring mobility. As robots enter everyday life via tasks as diverse as vacuum cleaning, nursing, and military transport, the requirement for maps that can enable mobility is obvious. Tasks such as surveying a disaster site during a search and rescue operation also require a map so that the location of possible victims or hazardous areas can be communicated.
2009, Proceedings - IEEE International Conference on Robotics and Automation
2008, Proceedings - IEEE International Conference on Robotics and Automation
2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
2007
Abstract This paper presents a tracking system for ego-motion estimation which fuses vision and inertial measurements using EKF and UKF (Extended and Unscented Kalman Filters), where a comparison of their performance has been done. It also considers the multi-rate nature of the sensors: inertial sensing is sampled at a fast sampling frequency while the sampling frequency of vision is lower.
2011
Abstract: In this paper we show how to carry out robust place recognition using both near and far information provided by a stereo camera. Visual appearance is known to be very useful in place recognition tasks. In recent years, it has been shown that taking geometric information also into account further improves system robustness. Stereo visual systems provide 3D information and texture of nearby regions, as well as an image of far regions.
2010
We propose to use a multi-camera rig for simultaneous localization and mapping (SLAM), providing flexibility in sensor placement on mobile robot platforms while exploiting the stronger localization constraints provided by omni-directional sensors. In this context, we present a novel probabilistic approach to data association, that takes into account that features can also move between cameras under robot motion.
In robotics, the term Simultaneous Localization And Mapping (SLAM) refers to the ability of a robot to create a map of the surrounding environment, and to localize itself in that map, by using its on-board sensors. SLAM is fundamental for mobile robots that want to efficiently solve common tasks such as search and rescue, space exploration and self-driving. Indeed, SLAM allows robots to perform more complex actions and reasoning, and for all these reasons it is considered an enabling technology toward the construction of truly autonomous mobile robots. For more than three decades, the robotics research community focused on the problem of localization and mapping. This led to the development of many different SLAM approaches, and solutions. However, while for 2D indoor applications SLAM reached stable results, when dealing with large-scale environments, or when extending the problem to 3D data, multiple issues arise. Under such assumptions, indeed, the complexity and the memory consumption increase significantly, in particular if the robot has to operate for long periods of time. Despite the recent advancements in the state-of-the-art of SLAM, current 3D localization and mapping methods can robustly handle only scenarios of moderate size (e.g. buildings). Moreover, in order for humans beings to be safe, novel solutions must be capable to detect and recover from failures, as well as be able to deal with noise such as dynamics aspects of the environment and perceptual aliasing. In the last years the demand for robots capable of helping human beings in their daily environment, including agriculture and autonomous driving, is constantly grow- ing. As a result of this, researchers concentrate on the development of long term SLAM systems that are able to cope with indoor/outdoor challenging environments such as urban scenarios, or underground tunnels. This also means that these systems must be designed to face sensor limitations, such as data sparsity and perceptual aliasing, and common noise factors like seasonal changes and moving objects. In this thesis we present multiple novel solutions that improve the accuracy, efficiency and robustness of the current state-of-the-art in localization and mapping, under such hard conditions. Modern SLAM systems are built as the aggregation and connection of multiple independent modules like algorithms for feature/key-point detection (data enhancement), measurement registration methods and optimization frameworks. A fundamental block is that one related to loop closures. By detecting areas already visited in the past by the robot, loop closing allows for a drastic reduction of the error affecting the global map estimate. Advancing any of these parts has the effect of improving the entire SLAM system. In this work we focused on developing innovative solutions for some of the most important modules composing modern localization and mapping approaches. We present a novel point cloud registration method based on an augmented error function associated to the alignment problem. Through an extensive experimental evaluation, our approach demonstrates the ability to outperform other state-of-the-art algorithms. We detail a robust and effective loop closing search heuristic that, based on the trajectory followed by the robot, significantly reduces the overall computation time. Additionally, similarly to visual SLAM approaches, it allows for recovering from inconsistent global map estimates due to miss detected closures. Such an algorithm is able to correctly map hard and harsh environments like catacombs. Also, we introduce a method that exploits 3D sensor data to build highly informative two dimensional measurements. These measurements are then used for achieving robust indoor/outdoor 2D SLAM. Our algorithm also makes efficient use of memory, making the approach particularly suitable for large-scale mapping. Finally, we present a very fast method for computing robust 3D geometric features from sparse sensor data. We demonstrate the effectiveness of that approach by performing 3D feature based SLAM with an autonomous car at the MCity test facility.
2007, The International Journal of …