Multivehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging

Li, Hao; Tsukada, Manabu; Nashashibi, Fawzi; Parent, Michel

doi:10.1109/tits.2014.2309639

Cited by 68 publications

(40 citation statements)

References 36 publications

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“…As well as type of approaches, another important parameter of map-merging problems is the type of the map. Feature [20] or occupancy-based [13] map representations are often in the foreground and occupancy-based maps are frequently preferred by researchers due to their being both true and metric representations of the world and presenting information about unexplored regions [21]. These advantages have led to occupancy-based maps being used in this study.…”

Section: Related Workmentioning

confidence: 99%

“…These advantages have led to occupancy-based maps being used in this study. In a similar manner to different map types, map merging can also be characterized by various approaches as based on features, iterative closest points (ICP), or overalldirect optimization [21][22][23]. This paper uses feature-based methods to match the regions of points of interest because the environment has sufficient properties for extraction.…”

Section: Related Workmentioning

confidence: 99%

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A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Durdu¹,

Korkmaz²

2019

Turk J Elec Eng & Comp Sci

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Map merging is a noteworthy phenomenon for cases such as search and rescue and disaster areas in which the duration is quite significant when gathering information about an environment. It is obvious that the total mapping time decreases if the number of agents (robots) increases. However, the use of multiple agents leads to problems such as task allocation schemes and the fusing of local maps. Examining the present methods, it is generally observed that the common features of local maps have been found and the global map is formed by obtaining related transformation between local maps. However, such implementations may be risky when local maps have symmetrical areas. Hence, a novel and semantic approach has been developed to solve this problem. The developed method counts on the reliability level of feature points. If relevant feature points are trusted, local maps are merged according to the best point or points. The simulation results from a robot operating system and a real-time experiment support the proposed method's efficiency, and mapping can be performed even for environments that have symmetrical similar parts and the task time can thus be reduced.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Durdu¹,

Korkmaz²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Similarly, to address the relative poses problem (problem a) for vehicles in outdoor environments, Li et al. use genetic algorithms (GA) to determine when the best match between the two maps occurs (Li, Tsukada, Nashashibi, & Parent, ). The experiments demonstrate that the maps have large overlaps, which guarantee success in map merging using GA.…”

Section: Current Solutions For Multiple‐robot Slammentioning

confidence: 99%

Multiple-Robot Simultaneous Localization and Mapping: A Review

et al. 2015

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Simultaneous localization and mapping (SLAM) in unknown GPS‐denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single‐robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state‐of‐the‐art multiple‐robot systems, with a major focus on multiple‐robot SLAM. Various issues and problems in multiple‐robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.

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“…Subsequently, two stochastic search approaches were proposed to solve this optimization problem [7,8]. Similarly, Li et al proposed an grid map merging approach based on the genetic algorithm [9]. Although these approaches may obtain the optimal rigid transformation, they are all time-consuming due to the nature of exhaustive search.…”

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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Multivehicle Cooperative Local Mapping: A Methodology Based on Occupancy Grid Map Merging

Cited by 68 publications

References 36 publications

A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

A novel map-merging technique for occupancy grid-based maps using multiple robots: a semantic approach

Multiple-Robot Simultaneous Localization and Mapping: A Review

Simultaneous Merging Multiple Grid Maps Using the Robust Motion Averaging

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