A moving grid cell based MCL algorithm for mobile robot localization

Wang, Yuefeng; Wu, Dan; Seifzadeh, Sepideh; Chen, Jingxi

doi:10.1109/robio.2009.5420461

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…For this purpose, the Monte Carlo Localization method (MCL) is used [15], implemented in the Player/Stage software platform installed on the robot's PC.…”

Section: Map Navigationmentioning

confidence: 99%

“…The laser range finder installed on the robot is used to measure distances to near obstacles and compare them with a building map, in order to estimate the robot's position. For this purpose, the Monte Carlo Localization method (MCL) is used [15], implemented in the Player/Stage software platform installed on the robot's PC.…”

Section: System Descriptionmentioning

confidence: 99%

“…When moving in a known environment, where a map can be used, with low smoke density (e.g., visibility of 5-8m) the laser range finder MCL (Monte Carlo Localization) algorithm [15] provides an easy way to navigate.  Fingerprinting techniques may be used with radiofrequency signals by placing beacons at known positions.…”

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confidence: 99%

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Multi-Sensor Localization and Navigation for Remote Manipulation in Smoky Areas

Martí

Sales

Marı́n

et al. 2013

International Journal of Advanced Robotic Systems

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When localizing mobile sensors and actuators in indoor environments laser meters, ultrasonic meters or even image processing techniques are usually used. On the other hand, in smoky conditions, due to a fire or building collapse, once the smoke or dust density grows, optical methods are not efficient anymore. In these scenarios other type of sensors must be used, such as sonar, radar or radiofrequency signals. Indoor localization in low-visibility conditions due to smoke is one of the EU GUARDIANS [1] project goals.The developed method aims to position a robot in front of doors, fire extinguishers and other points of interest with enough accuracy to allow a human operator to manipulate the robot's arm in order to actuate over the element. In coarse-grain localization, a fingerprinting technique based on ZigBee and WiFi signals is used, allowing the robot to navigate inside the building in order to get near the point of interest that requires manipulation. In fine-grained localization a remotely controlled programmable high intensity LED panel is used, which acts as a reference to the system in smoky conditions. Then, smoke detection and visual finegrained localization are used to position the robot with precisely in the manipulation point (e.g., doors, valves, etc.).

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“…For this purpose, the Monte Carlo Localization method (MCL) is used [15], implemented in the Player/Stage software platform installed on the robot's PC.…”

Section: Map Navigationmentioning

confidence: 99%

Section: System Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Sensor Localization and Navigation for Remote Manipulation in Smoky Areas

Martí

Sales

Marı́n

et al. 2013

International Journal of Advanced Robotic Systems

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“…For example, when the smoke density is low, laser range finder sensors are still able to localize the nodes (Monte Carlo Localization method [17]), with a small positioning error of approximately 10 cm, once the building map is available and the structure of the building has not been affected. When the laser range finder detects a significant amount of smoke it considers it as an obstacle, so the ZigBee fingerprinting methods become a suitable alternative to have an approximate idea of the position, as we will see in the next sections.…”

Section: Introductionmentioning

confidence: 99%

Localization of Mobile Sensors and Actuators for Intervention in Low-Visibility Conditions: The ZigBee Fingerprinting Approach

Martí

Sales

Marı́n

et al. 2012

International Journal of Distributed Sensor Networks

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Indoor localization in smoke conditions is one of the EU GUARDIANS project goals. When smoke density grows, optical sensors such as laser range finders and cameras cease to be efficient. Zigbee sensor networks provide an interesting approach due to the fact that radiofrequency signals are propagated easily in such conditions. Moreover, they permit having an alternative communication infrastructure to the emergency brigades, allowing also the implementation of localization algorithms for the mobile sensors, actuators, and firefighters. The overall localization method (i.e., ARIEL) aims to acquire the nodes position in real time during an intervention, using different sensor inputs such as laser, sonar, Zigbee, and Wifi signals. Moreover, a fine grained localization algorithm has been implemented to localize special points of interest such as emergency doors and fire extinguishers, using a Zigbee programmable high-intensity LED panel. This paper focuses on the Zigbee fingerprinting localization method used to obtain the position of the mobile sensors and actuators by training a database of radio signals for each scenario. Once this is done the proposed recognition method runs in a quite stable and accurate manner without needing any sophisticated hardware. Results compare the procedure with others such as KNN, and neural networks, demonstrating the feasibility of the method for a real emergency intervention.

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