Heterogeneous map merging using WiFi signals

Erinc, Gorkem; Balaguer, Benjamin; Carpin, Stefano

doi:10.1109/iros.2013.6697117

Cited by 3 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid map incorporates the matching result of both maps not by fusing the data, but by putting another layer atop the existing map (e.g., creating a grid-appearance map [83]).…”

Section: Resulting Global Mapsmentioning

confidence: 99%

“…To the best of author's knowledge the article by Erinc et al [83] presents the only research addressing both matching and fusion of fundamentally different map types. In the solution proposed by [83], additionally to the normal mapping, each robot is required to record the wireless signal strength of all access points. This wireless signal strength model is then later used to find overlap between different map types.…”

Section: Other Mapsmentioning

confidence: 99%

“…This wireless signal strength model is then later used to find overlap between different map types. The solution is tested by merging an occupancy grid and appearance-based map (undirected weighted graph, in which every vertex represents a camera image at a certain position), however, authors claim that any map types can be merged in this way [83].…”

Section: Other Mapsmentioning

confidence: 99%

See 2 more Smart Citations

Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

View full text Add to dashboard Cite

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.

show abstract

“…Hybrid map incorporates the matching result of both maps not by fusing the data, but by putting another layer atop the existing map (e.g., creating a grid-appearance map [83]).…”

Section: Resulting Global Mapsmentioning

confidence: 99%

Section: Other Mapsmentioning

confidence: 99%

Section: Other Mapsmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneous Map Merging: State of the Art

Andersone

2019

Robotics

View full text Add to dashboard Cite

show abstract

“…The algorithm's accuracy, speed, and low sensor requirements make it particularly suited for exploration scenarios of heterogeneous robot teams consisting of many sensor-deprived (and inexpensive) robots. Autonomous robot navigation and control using the WiFi localizer is an interesting future direction and is also being used to enable heterogeneous map merging [11]. Numerous directions for future research are possible.…”

Section: Discussionmentioning

confidence: 99%

Real-time WiFi localization of heterogeneous robot teams using an online random forest

2015

Self Cite

View full text Add to dashboard Cite

In this paper we present a WiFi-based solution to the localization and mapping problem for teams of heterogeneous robots operating in unknown environments. By exploiting wireless signal strengths broadcast from access points, a robot with a large sensor payload creates a WiFi signal map that can then be shared and utilized for localization by sensor-deprived robots. In our approach, WiFi localization is cast as a classification problem. An online clustering algorithm processes incoming WiFi signals that are then incorporated into an online random forest. The algorithm's robustness is increased by a Monte Carlo Localization algorithm whose sensor model exploits the results of the online random forest classification. The proposed algorithm is shown to run in real-time, allowing the robots to operate in completely unknown environments, where a priori information such as a blue-print or the access points' location is unavailable. A comprehensive set of experiments not only compares our approach with other algorithms, but also validates the results across different scenarios covering both indoor and outdoor environments.

show abstract

“…These methods are based on the robots' ability to meet and generate transformation hypotheses from a rendezvous, which is unfeasible for off-line methods. Erinc et al (2013) proposed a method to annotate heterogeneous maps with WIFI signal that provides cues for data association between maps. This means two essentially different maps are annotated by a shared landmark, which provides a seamless data association cue.…”

Section: Optimization Approachesmentioning

confidence: 99%

2D map alignment with region decomposition

Shahbandi

Magnusson

2018

Auton Robot

View full text Add to dashboard Cite

In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

show abstract

Heterogeneous map merging using WiFi signals

Cited by 3 publications

References 15 publications

Heterogeneous Map Merging: State of the Art

Heterogeneous Map Merging: State of the Art

Real-time WiFi localization of heterogeneous robot teams using an online random forest

2D map alignment with region decomposition

Contact Info

Product

Resources

About