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Parra-Barrero E, Vijayabaskaran S, Seabrook E, Wiskott L, Cheng S. A map of spatial navigation for neuroscience. Neurosci Biobehav Rev 2023; 152:105200. [PMID: 37178943 DOI: 10.1016/j.neubiorev.2023.105200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/13/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
Spatial navigation has received much attention from neuroscientists, leading to the identification of key brain areas and the discovery of numerous spatially selective cells. Despite this progress, our understanding of how the pieces fit together to drive behavior is generally lacking. We argue that this is partly caused by insufficient communication between behavioral and neuroscientific researchers. This has led the latter to under-appreciate the relevance and complexity of spatial behavior, and to focus too narrowly on characterizing neural representations of space-disconnected from the computations these representations are meant to enable. We therefore propose a taxonomy of navigation processes in mammals that can serve as a common framework for structuring and facilitating interdisciplinary research in the field. Using the taxonomy as a guide, we review behavioral and neural studies of spatial navigation. In doing so, we validate the taxonomy and showcase its usefulness in identifying potential issues with common experimental approaches, designing experiments that adequately target particular behaviors, correctly interpreting neural activity, and pointing to new avenues of research.
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Affiliation(s)
- Eloy Parra-Barrero
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany; International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Sandhiya Vijayabaskaran
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany
| | - Eddie Seabrook
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany
| | - Laurenz Wiskott
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany; International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Sen Cheng
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany; International Graduate School of Neuroscience, Ruhr University Bochum, Bochum, Germany.
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2
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Camera, LiDAR and Multi-modal SLAM Systems for Autonomous Ground Vehicles: a Survey. J INTELL ROBOT SYST 2022. [DOI: 10.1007/s10846-022-01582-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Lluvia I, Lazkano E, Ansuategi A. Active Mapping and Robot Exploration: A Survey. SENSORS (BASEL, SWITZERLAND) 2021; 21:2445. [PMID: 33918107 PMCID: PMC8037480 DOI: 10.3390/s21072445] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/21/2021] [Accepted: 03/28/2021] [Indexed: 11/16/2022]
Abstract
Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.
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Affiliation(s)
- Iker Lluvia
- Autonomous and Intelligent Systems Unit, Fundación Tekniker, 20600 Eibar, Gipuzkoa, Spain;
| | - Elena Lazkano
- Robotics and Autonomous Systems Group (RSAIT), Computer Science and Artificial Intelligence Department, Faculty of Informatics, University of the Basque Country (UPV/EHU), 20018 Donostia, Gipuzkoa, Spain;
| | - Ander Ansuategi
- Autonomous and Intelligent Systems Unit, Fundación Tekniker, 20600 Eibar, Gipuzkoa, Spain;
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4
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Liang Z, Zhu S, Fang F, Jin X. Simultaneous Localization and Mapping in a Hybrid Robot and Camera Network System. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-010-9446-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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5
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6
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Wu Y, Tang F, Li H. Image-based camera localization: an overview. Vis Comput Ind Biomed Art 2018; 1:8. [PMID: 32240389 PMCID: PMC7099558 DOI: 10.1186/s42492-018-0008-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 07/06/2018] [Indexed: 11/22/2022] Open
Abstract
Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.
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Affiliation(s)
- Yihong Wu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China.
| | - Fulin Tang
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
| | - Heping Li
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China
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7
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Abstract
Urban transportation in the next few decades will shift worldwide toward electrification and automation, with the final aim of increasing energy efficiency and safety for passengers. Such a big change requires strong collaboration and efforts among public administration, research and stakeholders in developing, testing and promoting these technologies in public transportation. Working in this direction, this work provides a review of the impact of the introduction of driverless electric minibuses, for the first and last mile transportation, in public service. More specifically, this paper covers the state of the art in terms of technological background for automation, energy efficiency via electrification and the current state of the legal framework in Europe with a focus on the Baltic Sea Region.
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8
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Moreno FA, Blanco JL, Gonzalez-Jimenez J. A constant-time SLAM back-end in the continuum between global mapping and submapping: application to visual stereo SLAM. Int J Rob Res 2016. [DOI: 10.1177/0278364915619238] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This work addresses the development and application of a novel approach, called sparser relative bundle adjustment (SRBA), which exploits the inherent flexibility of the relative bundle adjustment (RBA) framework to devise a continuum of strategies, ranging from RBA with linear graphs to classic bundle adjustment (BA) in global coordinates, where submapping with local maps emerges as a natural intermediate solution. This method leads to graphs that can be optimized in bounded time even at loop closures, regardless of the loop length. Furthermore, it is shown that the pattern in which relative coordinate variables are defined among keyframes has a significant impact on the graph optimization problem. By using the proposed scheme, optimization can be done more efficiently than in standard RBA, allowing the optimization of larger local maps for any given maximum computational cost. The main algorithms involved in the graph management, along with their complexity analyses, are presented to prove their bounded-time nature. One key advance of the present work is the demonstration that, under mild assumptions, the spanning trees for every single keyframe in the map can be incrementally built by a constant-time algorithm, even for arbitrary graph topologies. We validate our proposal within the scope of visual stereo simultaneous localization and mapping (SLAM) by developing a complete system that includes a front-end that seamlessly integrates several state-of-the-art computer vision techniques such as ORB features and bag-of-words, along with a decision scheme for keyframe insertion and a SRBA-based back-end that operates as graph optimizer. Finally, a set of experiments in both indoor and outdoor conditions is presented to test the capabilities of this approach. Open-source implementations of the SRBA back-end and the stereo front-end have been released online.
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Affiliation(s)
- Francisco-Angel Moreno
- MAPIR-UMA Group, Departamento Ingeniería de Sistemas y Automática, Universidad de Málaga, Málaga, Spain
| | | | - Javier Gonzalez-Jimenez
- MAPIR-UMA Group, Departamento Ingeniería de Sistemas y Automática, Universidad de Málaga, Málaga, Spain
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9
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Wang N, Ma S, Li B, Wang M, Zhao M. Map segmentation for simultaneous localization and mapping in ruins. Adv Robot 2016. [DOI: 10.1080/01691864.2015.1093428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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10
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Incremental topological segmentation for semi-structured environments using discretized GVG. Auton Robots 2014. [DOI: 10.1007/s10514-014-9398-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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Vision-based topological mapping and localization by means of local invariant features and map refinement. ROBOTICA 2014. [DOI: 10.1017/s0263574714000782] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
SUMMARYWe propose an appearance-based approach for topological visual mapping and localization using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of randomized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. In order to avoid redundant information in the resulting maps, we also present a map refinement framework, which takes into account the visual information stored in the map for refining the final topology of the environment. These refined maps save storage space and improve the execution times of localizations tasks. The approach is validated using image sequences from several environments and compared with the state-of-the-art FAB-MAP 2.0 algorithm.
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Abstract
SUMMARYOne of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.
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13
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Schwendner J, Joyeux S, Kirchner F. Using Embodied Data for Localization and Mapping. J FIELD ROBOT 2013. [DOI: 10.1002/rob.21489] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jakob Schwendner
- Robotics Innovation Center (RIC); German Research Center for Artificial Intelligence (DFKI); 28359 Bremen Germany
| | - Sylvain Joyeux
- Robotics Innovation Center (RIC); German Research Center for Artificial Intelligence (DFKI); 28359 Bremen Germany
| | - Frank Kirchner
- Robotics Innovation Center (RIC); German Research Center for Artificial Intelligence (DFKI); 28359 Bremen Germany
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14
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Labbe M, Michaud F. Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation. IEEE T ROBOT 2013. [DOI: 10.1109/tro.2013.2242375] [Citation(s) in RCA: 239] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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15
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Abstract
SUMMARYThis paper presents a new approach to matching occupancy grid maps by means of finding correspondences between a set of sparse features detected in the maps. The problem is stated here as a special instance of generic image registration. To cope with the uncertainty and ambiguity that arise from matching grid maps, we introduce a modified RANSAC algorithm which searches for a dynamic number of internally consistent subsets of feature pairings from which to compute hypotheses about the translation and rotation between the maps. By providing a (possibly multi-modal) probability distribution of the relative pose of the maps, our method can be seamlessly integrated into large-scale mapping frameworks for mobile robots. This paper provides a benchmarking of different detectors and descriptors, along extensive experimental results that illustrate the robustness of the algorithm with a 97% success ratio in loop-closure detection for ~1700 matchings between local maps obtained from four publicly available datasets.
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16
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Abstract
In this paper we propose a vision-based online mapping of large-scale environments. Our approach uses a hybrid representation of a fully metric Euclidean environment map and a topological map. This novel hybrid representation facilitates our scalable online hierarchical bundle adjustment approach. The proposed method achieves scalability by solving the local registration through embedding neighboring keyframes and landmarks into a Euclidean space. The global adjustment is performed on a segmentation of the keyframes and posed as the iterative optimization of the arrangement of keyframes in each segment and the arrangement of rigidly moving segments. The iterative global adjustment is performed concurrently with the local registration of the keyframes in a local map. Thus, the map is always locally metric around the current location, and likely to be globally consistent. Loop closures are handled very efficiently benefiting from the topological nature of the map and overcoming the loss of the metric map properties of previous approaches. The effectiveness of the proposed method is demonstrated in real-time on various challenging video sequences.
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Affiliation(s)
- Jongwoo Lim
- Division of Computer Science and Engineering, Hanyang University, Seoul, Korea
| | - Jan-Michael Frahm
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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17
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Yokozuka M, Matsumoto O. Sub-Map Dividing and Realignment FastSLAM by Blocking Gibbs MCEM for Large-Scale 3-D Grid Mapping. Adv Robot 2012. [DOI: 10.1080/01691864.2012.695892] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Masashi Yokozuka
- a Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology , Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki , Japan
| | - Osamu Matsumoto
- a Intelligent Systems Research Institute, National Institute of Advanced Industrial Science and Technology , Tsukuba Central 2, 1-1-1 Umezono, Tsukuba, Ibaraki , Japan
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18
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Maddern W, Milford M, Wyeth G. CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory. Int J Rob Res 2012. [DOI: 10.1177/0278364912438273] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper describes a new system, dubbed Continuous Appearance-based Trajectory Simultaneous Localisation and Mapping (CAT-SLAM), which augments sequential appearance-based place recognition with local metric pose filtering to improve the frequency and reliability of appearance-based loop closure. As in other approaches to appearance-based mapping, loop closure is performed without calculating global feature geometry or performing 3D map construction. Loop-closure filtering uses a probabilistic distribution of possible loop closures along the robot’s previous trajectory, which is represented by a linked list of previously visited locations linked by odometric information. Sequential appearance-based place recognition and local metric pose filtering are evaluated simultaneously using a Rao–Blackwellised particle filter, which weights particles based on appearance matching over sequential frames and the similarity of robot motion along the trajectory. The particle filter explicitly models both the likelihood of revisiting previous locations and exploring new locations. A modified resampling scheme counters particle deprivation and allows loop-closure updates to be performed in constant time for a given environment. We compare the performance of CAT-SLAM with FAB-MAP (a state-of-the-art appearance-only SLAM algorithm) using multiple real-world datasets, demonstrating an increase in the number of correct loop closures detected by CAT-SLAM.
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Affiliation(s)
- Will Maddern
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia
| | - Michael Milford
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia
| | - Gordon Wyeth
- School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia
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19
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Abstract
SUMMARYVision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm.
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20
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Tully S, Kantor G, Choset H. A unified Bayesian framework for global localization and SLAM in hybrid metric/topological maps. Int J Rob Res 2012. [DOI: 10.1177/0278364911433617] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
We present a unified filtering framework for hybrid metric/topological robot global localization and SLAM. At a high level, our method relies on a topological graph representation whose vertices define uniquely identifiable places in the environment and whose edges define feasible paths between them. At a low level, our method generalizes to any detailed metric submapping technique. The filtering framework we present is designed for multi-hypothesis estimation in order to account for ambiguity when closing loops and to account for uniform uncertainty when initializing pose estimates. Our implementation tests multiple topological hypotheses through the incremental construction of a hypothesis forest with each leaf representing a possible graph/state pair at the current time step. Instead of using a heuristic approach to accept or reject hypotheses, we propose a novel Bayesian method that computes the posterior probability of each hypothesis. In addition, for every topological hypothesis, a metric estimate is maintained with a local Kalman filter. Careful pruning of the hypothesis forest keeps the growing number of hypotheses under control while a garbage-collector hypothesis is used as a catch-all for pruned hypotheses. This enables the filter to recover from unmodeled disturbances such as the kidnapped robot problem.
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Affiliation(s)
- Stephen Tully
- Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - George Kantor
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Howie Choset
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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21
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A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment. SENSORS 2011; 11:10197-219. [PMID: 22346639 PMCID: PMC3274281 DOI: 10.3390/s111110197] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2011] [Revised: 09/27/2011] [Accepted: 10/27/2011] [Indexed: 11/17/2022]
Abstract
Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale Simultaneous Localization and Mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a Combined SLAM—an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed Combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the Combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset.
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22
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Abstract
SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.
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23
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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. We present efficient sampling mechanisms using data-driven proposals and prior distributions on topologies that further enable OPTM’s operation in an online manner. OPTM can incorporate various sensors seamlessly, as is demonstrated by our use of appearance, laser, and odometry measurements. OPTM is the first topological mapping algorithm that is theoretically accurate, systematic, sensor independent, and online, and thus advances the state of the art significantly. We evaluate the algorithm on a robot in diverse environments.
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Affiliation(s)
| | - Frank Dellaert
- College of Computing, Georgia Institute of Technology, Atlanta, GA, USA
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24
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25
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Abstract
We describe a new formulation of appearance-only SLAM suitable for very large scale place recognition. The system navigates in the space of appearance, assigning each new observation to either a new or a previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop-closure detection over a 1000 km trajectory, with mean filter update times of 14 ms. The scalability of the system is achieved by defining a sparse approximation to the FAB-MAP model suitable for implementation using an inverted index. Our formulation of the problem is fully probabilistic and naturally incorporates robustness against perceptual aliasing. We also demonstrate that the approach substantially outperforms the standard term-frequency inverse-document-frequency (tf-idf) ranking measure. The 1000 km data set comprising almost a terabyte of omni-directional and stereo imagery is available for use, and we hope that it will serve as a benchmark for future systems.
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26
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Fairfield N, Wettergreen D, Kantor G. Segmented SLAM in three-dimensional environments. J FIELD ROBOT 2010. [DOI: 10.1002/rob.20320] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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27
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Beeson P, Modayil J, Kuipers B. Factoring the Mapping Problem: Mobile Robot Map-building in the Hybrid Spatial Semantic Hierarchy. Int J Rob Res 2009. [DOI: 10.1177/0278364909100586] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We propose a factored approach to mobile robot map-building that handles qualitatively different types of uncertainty by combining the strengths of topological and metrical approaches. Our framework is based on a computational model of the human cognitive map; thus it allows robust navigation and communication within several different spatial ontologies. This paper focuses exclusively on the issue of map-building using the framework. Our approach factors the mapping problem into natural sub-goals: building a metrical representation for local small-scale spaces; finding a topological map that represents the qualitative structure of large-scale space; and (when necessary) constructing a metrical representation for large-scale space using the skeleton provided by the topological map. We describe how to abstract a symbolic description of the robot’s immediate surround from local metrical models, how to combine these local symbolic models in order to build global symbolic models, and how to create a globally consistent metrical map from a topological skeleton by connecting local frames of reference.
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Affiliation(s)
- Patrick Beeson
- Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Taylor Hall 2124 Austin, TX, USA 78712-0233,
| | - Joseph Modayil
- Department of Computer Science, University of Rochester, PO Box 270226 734 Computer Studies Bldg. Rochester, NY, USA, 14627-0226,
| | - Benjamin Kuipers
- Department of Electrical Engineering and Computer Science, University of Michigan, 2260 Hayward Street, Ann Arbour, MI 48109-2121, USA,
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28
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Pinies P, Tardos J. Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision. IEEE T ROBOT 2008. [DOI: 10.1109/tro.2008.2004636] [Citation(s) in RCA: 76] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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