Sensor Fusion for Mobile Robot Navigation

Kam, Moshe; Zhu, Xiuli; Kalata, P.

doi:10.1109/jproc.1997.554212

Cited by 216 publications

(91 citation statements)

References 47 publications

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“…Various L5 user refinement decision support techniques are proposed [77,78,79]. Finally system design issues are presented with metrics [80] and evaluation [81].…”

Section: Hlif As An Emerging Topicmentioning

confidence: 99%

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

Blasch

Llinas

Lambert

et al. 2010

2010 13th International Conference on Information Fusion

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-The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary HLIF advances and developments to determine unsolved grand challenges and issues. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and high-level information fusion (control, situational understanding, and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), systems design (scenario-based, user-based, distributedagent) The HLIF panel discussion's goal is to highlight the unsolved problems and concerns to motivate the information fusion community towards systems-level solutions. The panelists' expert perspectives are based on three areas: (1) previous panel discussions and summaries, (2) an integrated list of HLIF challenges, and (3) companion papers presented at the Fusion2010 conference (note we switch to Fusion10 to refer to the conference). Previous Related Panel DiscussionsPanel discussions provide a valuable resource to the community to overview the current techniques and provide areas of concern for future research. Previous Fusion Conference panel discussion papers related to HLIF include knowledge representation (Fusion05) [7], resource management coordination with situation and threat assessment (Fusion06) [8, 9, 10], agent-based design (Fusion07) [11], and HLIF challenges (Fusion08) [12]. Three panel discussions were conducted at Fusion09 without papers: Many of the authors of this Fusion10 HLIF panel coordinated on previous publications, but continual refinement of HLIF contemporary are desired. The panel discussion follows from a day-long event special session. There are most likely other papers at Fusion10 that are related that would validate good questions from the audience to the panelists. Many of the participants to the special session would be encouraged to voice their opinions and questions to the moderated panel.

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“…Various L5 user refinement decision support techniques are proposed [77,78,79]. Finally system design issues are presented with metrics [80] and evaluation [81].…”

Section: Hlif As An Emerging Topicmentioning

confidence: 99%

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

Blasch

Llinas

Lambert

et al. 2010

2010 13th International Conference on Information Fusion

View full text Add to dashboard Cite

show abstract

“…This has led to the development of a number of algorithms for mobile robot localization by "fusing" multiple sensor feeds. There are a number of approaches to sensor fusion [13] for robot localization, including merging multiple sensor feeds at the lowest level before being processed homogeneously, and hierarchical approaches to fuse state estimates derived independently from multiple sensors.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multi-sensor Mobile Robot Localization for Diverse Environments

Biswas

Veloso

2014

RoboCup 2013: Robot World Cup XVII

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Abstract. Mobile robot localization with different sensors and algorithms is a widely studied problem, and there have been many approaches proposed, with considerable degrees of success. However, every sensor and algorithm has limitations, due to which we believe no single localization algorithm can be "perfect," or universally applicable to all situations. Laser rangefinders are commonly used for localization, and state-of-theart algorithms are capable of achieving sub-centimeter accuracy in environments with features observable by laser rangefinders. Unfortunately, in large scale environments, there are bound to be areas devoid of features visible by a laser rangefinder, like open atria or corridors with glass walls. In such situations, the error in localization estimates using laser rangefinders could grow in an unbounded manner. Localization algorithms that use depth cameras, like the Microsoft Kinect sensor, have similar characteristics. WiFi signal strength based algorithms, on the other hand, are applicable anywhere there is dense WiFi coverage, and have bounded errors. Although the minimum error of WiFi based localization may be greater than that of laser rangefinder or depth camera based localization, the maximum error of WiFi based localization is bounded and less than that of the other algorithms. Hence, in our work, we analyze the strengths of localization using all three sensors -using a laser rangefinder, a depth camera, and using WiFi. We identify sensors that are most accurate at localization for different locations on the map. The mobile robot could then, for example, rely on WiFi localization more in open areas or areas with glass walls, and laser rangefinder and depth camera based localization in corridor and office environments.

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“…According to Kam, Zhu, and Kalata, it is unlikely that one technique or one architecture will provide a uniformly superior solution [3]. Thus, there exist numerous architectures and models for sensor fusion in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Review on System Architectures for Sensor Fusion Applications

Elmenreich

2007

Software Technologies for Embedded and Ubiquitous Systems

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Abstract. In the literature there exist many proposed architectures for sensor fusion applications. This paper briefly reviews some of the most common approaches, i. e., the JDL fusion architecture, the Waterfall model, the Intelligence cycle, the Boyd loop, the LAAS architecture, the Omnibus model, Mr. Fusion, the DFuse framework, and the Time-Triggered Sensor Fusion Model, and categorizes them into abstract models, generic and rigid architectures. While an abstract model does not guide the designer in the concrete implementation, the generic architectures provide a generic design but leave open several design decisions regarding operating system, hardware, communication system, or database system. Rigid architectures specify at least some of these aspects and therefore provide existing hardware designs, tools, and source code at the cost of flexibility.

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Sensor Fusion for Mobile Robot Navigation

Cited by 216 publications

References 47 publications

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

Multi-sensor Mobile Robot Localization for Diverse Environments

A Review on System Architectures for Sensor Fusion Applications

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