A comparison of SLAM implementations for indoor mobile robots

Ouellette, R.; Hirasawa, K.

doi:10.1109/iros.2007.4399575

Cited by 25 publications

(12 citation statements)

References 7 publications

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“…In the robot navigation community, much effort has been devoted to the problem of simultaneous localization and mapping (SLAM) using onboard sensors (Dissanayake et al 2001;Ouellette and Hirasawa 2007). These techniques serve the dual purpose of map-building and robot localization.…”

Section: Online Slammentioning

confidence: 99%

Simultaneous people tracking and robot localization in dynamic social spaces

Glas¹,

Morales²,

Kanda³

et al. 2015

Auton Robot

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Accurate robot localization and people tracking are necessary for deploying service robots in crowded everyday environments such as shopping malls, and features like product displays change over time, making map-based localization using on-board sensors difficult. We propose the use of an external sensor system to track people together with one or more robots. This approach is more robust to occlusions than on-board sensing and is unaffected by changing map features. In our system, laser range finders track people and robots in the environment, and odometry data is used to associate each robot with a tracked entity and correct the robot's pose. Techniques are also presented for identifying and recovering from tracking errors. Simulation results show that our system can outperform localization using on-board sensors, both in tracking accuracy and in automatic recovery from errors. We demonstrate our system's effectiveness in simulation, in a controlled experiment in a real shopping Electronic supplementary material The online version of this article (

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Section: Online Slammentioning

confidence: 99%

Simultaneous people tracking and robot localization in dynamic social spaces

Glas¹,

Morales²,

Kanda³

et al. 2015

Auton Robot

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“…Simultaneous Localisation and Mapping (SLAM) has been developed to localise the robot while building a map of the environment [23], with promising results. But according to [24], different implementations of SLAM pose certain limitations. SLAM works better in corridors and areas which are less populated and where there are predictable features in the environment.…”

Section: Robot Localisationmentioning

confidence: 99%

Using an autonomous robot to maintain privacy in assistive environments

Armbrust¹,

Mehdi²,

Reichardt³

et al. 2011

Security Comm Networks

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In our societies, the number of senior citizens living on their own is increasing steadily. The lack of permanent attention results in the late detection of emergency situations. Labour-intensive care is already a high burden for the society, therefore it seems reasonable to promote technology that helps to detect and react in case of emergency situations that elderly people may encounter.In the last decade, assistive environments have been established by integrating surveillance devices into the living environments giving remote operators access to monitor the senior inhabitant at home for detecting emergency situations. However, due to poor privacy in terms of intrusion into the private life of an elderly person, there will be an unfavourably low acceptance of such systems.This paper introduces a two-stage strategy and proposes to replace a possibly large number of humancontrolled monitoring devices by a single autonomous mobile system. The first stage will be performed by the autonomous system to detect an emergency situation. The human operator will be obligatory only at the final stage when the system assumes that an emergency has occurred and the final evaluation of the situation is required. The self-assessment will reduce the human factor related to privacy issues.

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“…Various techniques have been devised for precise mapping and navigation, a more recent methodology, Simultaneous Localisation and Mapping (SLAM) has been developed to localise the robot while building a map of the environment [8]. Although promising results are being achieved but according to [10], different implementations of SLAM pose certain limitations. SLAM works better in corridors and areas which are less populated and where there are predictable features in the environment.…”

Section: The Mapping Systemmentioning

confidence: 99%

Methodology for robot mapping and navigation in assisted living environments

Mehdi

Armbrust

Koch

et al. 2009

Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments

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Robust navigation in living environments demands high requirements on the control system of a robot. Due to typically narrow passages between obstacles, precise navigation is required. To achieve accuracy in navigation, detailed representation of the environment around the robot needs to be developed. Robust and precise mapping of the environment helps in overcoming the dynamics in the living environment like movement of furniture and human beings. Prompt recovery from unreachable paths while navigating is also an essential component of the living environment robots. This paper describes a behaviour-based navigation system in assisted living environments. The navigation system uses a grid map created from data obtained from laser scanner and ultrasonic sensors mounted on a small sized robot, ARTOS. ARTOS is specially designed for indoor living environments able to navigate through narrow corridors and closely placed furniture in the living environment.

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A comparison of SLAM implementations for indoor mobile robots

Cited by 25 publications

References 7 publications

Simultaneous people tracking and robot localization in dynamic social spaces

Simultaneous people tracking and robot localization in dynamic social spaces

Using an autonomous robot to maintain privacy in assistive environments

Methodology for robot mapping and navigation in assisted living environments

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