Robust online map merging system using laser scan matching and omnidirectional vision

Tungadi, Fredy; Lui, Wen Lik Dennis; Kleeman, Lindsay; Jarvis, Ray

doi:10.1109/iros.2010.5654446

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…In Ref. [35] the authors merge LIDAR scan matching and omnidirectional vision to form a more robust mapping algorithm.…”

Section: Scan Matchingmentioning

confidence: 99%

LIDAR-based surface following control for unmanned aerial vehicles

Simms¹

2023

Preprint

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A simulation study is performed on a quadcopter which uses a LIDAR sensor to allow a quadcopter to navigate along and maintain a set distance from an unknown vertical surface. The dynamic equations of a quadcopter are linearized about the hovering equilibrium. For the purpose of design, all surfaces are assumed to be flat and any variations in shape are considered to be disturbances. The design process begins with the development of a potential field control design to allow the quadcopter to autonomously follow a flat surface, while maintaining a desired distance from the surface. To allow the quadcopter to follow a curved surface, the potential field technique is modified to maintain the xb axis parallel to the surface. Finally a wall following technique that directly uses the minimum range measurement to maintain the distance from the surface is developed. To simulate the control designs, a non-linear quadcopter model is used along with a model of a 2D scanning LIDAR sensor. The potential field control technique tracks flat surfaces with no steadystate error, though when curved surface following is added, a tracking error problem occurs due to measurement noise. The wall following design proves to be the superior surface following technique with greater robustness to steady-state error and results in relatively small tracking errors when navigating sinusoidal surfaces and corners.

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“…In Ref. [35] the authors merge LIDAR scan matching and omnidirectional vision to form a more robust mapping algorithm.…”

Section: Scan Matchingmentioning

confidence: 99%

LIDAR-based surface following control for unmanned aerial vehicles

Simms¹

2023

Preprint

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“…In addition, since the accuracy of map merging depends on direct bilateral observation measurements, highly accurate sensors and data processing techniques are required, which increases the cost of configuring the multi-robot systems. In common objectbased map merging methods [3], [9], map merging is conducted by recognizing common objects that are known to each other between robots and their relative locations and directions. However, since they need to predetermine the number, type and location of common objects, they are not practical in unknown environments.…”

Section: B Related Workmentioning

confidence: 99%

Extended Spectra-Based Grid Map Merging With Unilateral Observations for Multi-Robot SLAM

Lee

2021

IEEE Access

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This paper deals with the problem of grid map merging in multi-robot SLAM (simultaneous positioning and mapping) where the initial relative pose between robots is unknown. When robots encounter each other, it is easy to obtain a map transformation between robots for grid map merging if bilateral observation measurements are available between robots. However, since the bilateral observation measurements are obtained by encounters between robots, they may limit the availability of using multi-robot systems. To overcome the limitation, spectra-based map merging can be applied without any observation measurements between robots. However, it requires sufficient overlapping areas between indivisual maps of robots, which can also limit the availability of using multi-robot systems. In this paper, therefore, to overcome both limitations, an extension of spectra-based map merging using not bilateral but unilateral observation measurements. The proposed method was tested with datasets obtained from real experiments with mobile robots equipped with a sensor fusion system which can obtain unilateral observation measurements to other robots. Experimental results showed that the proposed map merging method works successfully without any bilateral observation measurements.

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“…However, both accuracy and reliability are known to be problematic in large-scale localization (Park and Roh 2016). Alternatively, with indirect metric methods, individual maps are merged via aligning them using a variety of approaches such as random walks, laser scan integration methods or genetic algorithms (Carpin et al 2005;Birk and Carpin 2006;Amigoni et al 2006;Adluru et al 2008;Ma et al 2008;Tungadi et al 2010;Marjovi et al 2012;Tomono 2013). However, the merging process has proven to be costly.…”

Section: Related Literaturementioning

confidence: 99%

Merging of appearance-based place knowledge among multiple robots

Karaoğuz

Bozma

2020

Auton Robot

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If robots can merge the appearance-based place knowledge of other robots with their own, they can relate to these places even if they have not previously visited them. We have investigated this problem using robots with compatible visual sensing capabilities and with each robot having its individual long-term place memory. Here, each place refers to a spatial region as defined by a collection of appearances and in the place memory, the knowledge is organized in a tree hierarchy. In the proposed merging approach, the hierarchical organization plays a key role-as it corresponds to a nested sequence of hyperspheres in the appearance space. The merging proceeds by considering the extent of overlap of the respective nested hyperspheres-starting with the largest covering hypersphere. Thus, differing from related work, knowledge is merged in as large chunks as possible while the hierarchical structure is preserved accordingly. As such, the merging scales better as the extent of knowledge to be merged increases. This is demonstrated in an extensive set of multirobot experiments where robots share their knowledge and then use their merged knowledge when visiting these places.

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Robust online map merging system using laser scan matching and omnidirectional vision

Cited by 18 publications

References 19 publications

LIDAR-based surface following control for unmanned aerial vehicles

LIDAR-based surface following control for unmanned aerial vehicles

Extended Spectra-Based Grid Map Merging With Unilateral Observations for Multi-Robot SLAM

Merging of appearance-based place knowledge among multiple robots

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