Merging grid maps of different resolutions by scaling registration

Ma, Liang; Zhu, Jihua; Zhu, Li; Du, Shaoyi; Cui, Jingru

doi:10.1017/s0263574715000168

Cited by 24 publications

(23 citation statements)

References 30 publications

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“…To merge different scaled occupancy grids SIFT (scale-invariant feature transform) features are extracted from both maps and used to find a transformation by using nearest neighbor algorithms with minimum Euclidean distance. Several other works have also later addressed different scale occupancy grid merging with different methods [81,82,86].…”

Section: Metric Map Mergingmentioning

confidence: 99%

“…Ma et al [86] and Ferrao et al [82] also use SIFT features to find common key-points in the maps with some differences in how map transformation is found and optimized. Ma et al [86] uses the random sample consensus (RANSAC) algorithm to find the initial transformation between maps and then optimizes it by solving an objective function based on non-common areas of the maps with trimmed and scaling iterative closest point (TsICP) algorithm. Ferrao et al [82] addresses the problem similar to [86] with some slight differences in transformation computation.…”

Section: Metric Map Mergingmentioning

confidence: 99%

“…Several methods address only different scale grid map merging, but do not account for significantly different levels of noise [23,81,82,86]. There is one method, which is robust against noise and dissimilarities between maps, but it is limited by computation time and environment characteristics [87].…”

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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Section: Metric Map Mergingmentioning

confidence: 99%

Section: Metric Map Mergingmentioning

confidence: 99%

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Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

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“…According to our previous work [5], the problem of merging grid maps with different resolutions can be reasonably viewed as the scaling registration of the partially overlapping point sets. Therefore, the sTrICP algorithm can be applied to the registration of extracted edge point sets and obtain the accurate scaling transformation.…”

Section: Application: Grid Map Mergingmentioning

confidence: 99%

Effective scaling registration approach by imposing emphasis on scale factor

Zhu

et al. 2018

Electron. lett.

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This paper proposes an effective approach for the scaling registration of m-D point sets. Different from the rigid transformation, the scaling registration can not be formulated into the common least square function due to the ill-posed problem caused by the scale factor. Therefore, this paper designs a novel objective function for the scaling registration problem. The appearance of this objective function is a rational fraction, where the numerator item is the least square error and the denominator item is the square of the scale factor. By imposing the emphasis on scale factor, the ill-posed problem can be avoided in the scaling registration. Subsequently, the new objective function can be solved by the proposed scaling iterative closest point (ICP) algorithm, which can obtain the optimal scaling transformation. For the practical applications, the scaling ICP algorithm is further extended to align partially overlapping point sets. Finally, the proposed approach is tested on public data sets and applied to merging grid maps of different resolutions.Experimental results demonstrate its superiority over previous approaches on efficiency and robustness.

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“…To address the robustness issue, Saeedi et al proposed the improved grid map merging approach based on the Hough transform, which can merge grid map pair even with low overlapping percentage [17]. To merge grid maps with different resolutions, Ma et al put forward an image registration based approach [18], which can determine whether one of the two maps should be minified or magnified in order to be merged with the other. It seems that many proposed approaches can merge grid map pair with good accuracy and efficiency, but few merging approaches can really accomplish simultaneous merging multiple grid maps.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Merging Multiple Grid Maps Using the Robust Motion Averaging

Jiang

Zhu

et al. 2018

J Intell Robot Syst

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Mapping in the GPS-denied environment is an important and challenging task in the field of robotics. In the large environment, mapping can be significantly accelerated by multiple robots exploring different parts of the environment. Accordingly, a key problem is how to integrate these local maps built by different robots into a single global map. In this paper, we propose an approach for simultaneous merging of multiple grid maps by the robust motion averaging. The main idea of this approach is to recover all global motions for map merging from a set of relative motions. Therefore, it firstly adopts the pair-wise map merging method to estimate relative motions for grid map pairs. To obtain as many reliable relative motions as possible, a graph-based sampling scheme is utilized to efficiently remove unreliable relative motions obtained from the pair-wise map merging. Subsequently, the accurate global motions can be recovered from the set of reliable relative motions by the motion averaging. Experimental results carried on real robot data sets demonstrate that proposed approach can achieve simultaneous merging of multiple grid maps with good performances.

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Merging grid maps of different resolutions by scaling registration

Cited by 24 publications

References 30 publications

Heterogeneous Map Merging: State of the Art

Heterogeneous Map Merging: State of the Art

Effective scaling registration approach by imposing emphasis on scale factor

Simultaneous Merging Multiple Grid Maps Using the Robust Motion Averaging

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