A pose graph-based localization system for long-term navigation in CAD floor plans

Boniardi, Federico; Caselitz, Tim; Kümmerle, Rainer; Burgard, Wolfram

doi:10.1016/j.robot.2018.11.003

Cited by 42 publications

(35 citation statements)

References 15 publications

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“…Other works directly detect changes and correspondences between robot views or images [6], [7], [8], [9], [10], [11], [12]. Full RGB-D views are used in [6] to build a map of the robot world.…”

Section: Related Workmentioning

confidence: 99%

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Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Derner

Gómez

Hernandez

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of lifelong autonomy. In computer vision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.

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Section: Related Workmentioning

confidence: 99%

“…Change candidates are computed with the Mahalanobis distance and filtered to eliminate outliers. Authors in [11] proposed a 2D LiDAR-based framework for long-term indoor localization on prior floor plans. The system combines graph-based mapping techniques and Bayes filtering to detect significant changes in the environment.…”

Section: Related Workmentioning

confidence: 99%

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Derner

Gómez

Hernandez

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

View full text Add to dashboard Cite

show abstract

“…Another research direction in the heterogeneous map matching is the matching of the robot map with some kind of prior map (building plan, sketch, CAD plan) [89,90].…”

Section: Metric Map Mergingmentioning

confidence: 99%

“…Boniardi et al [89] uses the graph-based SLAM to build a pose graph map that is consistent with a prior: CAD floor plan. The CAD map lacks important information about the environment (the room contents), but is useful as a prior for the building wall configurations.…”

Section: Metric Map Mergingmentioning

confidence: 99%

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Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

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Multi-robot mapping and environment modeling have several advantages that makeit an attractive alternative to the mapping with a single robot: faster exploration, higherfault tolerance, richer data due to different sensors being used by different systems. However,the environment modeling with several robotic systems operating in the same area causes problemsof higher-order—acquired knowledge fusion and synchronization over time, revealing the sameenvironment properties using different sensors with different technical specifications. While theexisting robot map and environment model merging techniques allow merging certain homogeneousmaps, the possibility to use sensors of different physical nature and different mapping algorithms islimited. The resulting maps from robots with different specifications are heterogeneous, and eventhough some research on how to merge fundamentally different maps exists, it is limited to specificapplications. This research reviews the state of the art in homogeneous and heterogeneous mapmerging and illustrates the main research challenges in the area. Six factors are identified thatinfluence the outcome of map merging: (1) robotic platform hardware configurations, (2) maprepresentation types, (3) mapping algorithms, (4) shared information between robots, (5) relativepositioning information, (6) resulting global maps.

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A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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A pose graph-based localization system for long-term navigation in CAD floor plans

Cited by 42 publications

References 15 publications

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection

Heterogeneous Map Merging: State of the Art

A systematic literature review on long‐term localization and mapping for mobile robots

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