Applying domain knowledge to SLAM using virtual measurements

Trevor, Alexander J. B.; Rogers, John G.; Nieto, Carlos; Christensen, Henrik I.

doi:10.1109/robot.2010.5509497

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…In previous work, we have demonstrated a feature-based mapping system capable of mapping features such as 2D lines [22]. We also demonstrated how surfaces could be detected in point clouds and mapped in a global map frame, however these surfaces were not used as landmarks.…”

Section: Approachmentioning

confidence: 95%

Planar surface SLAM with 3D and 2D sensors

Trevor¹,

Rogers²,

Christensen³

2012

2012 IEEE International Conference on Robotics and Automation

121

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We present an extension to our feature based mapping technique that allows for the use of planar surfaces such as walls, tables, counters, or other planar surfaces as landmarks in our mapper. These planar surfaces are measured both in 3D point clouds, as well as 2D laser scans. These sensing modalities compliment each other well, as they differ significantly in their measurable fields of view and maximum ranges. We present experiments to evaluate the contributions of each type of sensor.

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Section: Approachmentioning

confidence: 95%

Planar surface SLAM with 3D and 2D sensors

Trevor¹,

Rogers²,

Christensen³

2012

2012 IEEE International Conference on Robotics and Automation

121

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“…This type of relationship between landmarks can be thought of as a virtual measurement between the landmarks. In previous work [38], we introduced this type of measurement and described their use. The current OmniMapper framework does not include any implementations of virtual measurements, though support may be added as future work.…”

Section: B Common Measurement Typesmentioning

confidence: 99%

OmniMapper: A modular multimodal mapping framework

Trevor

Rogers

Christensen

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Simultaneous Localization and Mapping (SLAM)is not a problem with a one-size-fits-all solution. The literature includes a variety of SLAM approaches targeted at different environments, platforms, sensors, CPU budgets, and applications. We propose OmniMapper, a modular multimodal framework and toolbox for solving SLAM problems. The system can be used to generate pose graphs, do feature-based SLAM, and also includes tools for semantic mapping. Multiple measurement types from different sensors can be combined for multimodal mapping. It is open with standard interfaces to allow easy integration of new sensors and feature types. We present a detailed description of the mapping approach, as well as a software framework that implements this, and present detailed descriptions of its applications to several domains including mapping with a service robot in an indoor environment, largescale mapping on a PackBot, and mapping with a handheld RGBD camera.

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“…The system has also been used to support semantic mapping, where additional high-level information is included within a map as it is observed by the robot. The first type of high level information we have included is called virtual measurements [22], where parallel and perpendicular constraints are included between walls which exhibit these relationships, and visual features which appear to be coplanar with walls are constrained to lie upon them. This technique has also been used with object recognition for building semantic maps together with a powerful cue for loop closure in [16], and object recognition has been used with the map to perform place recognition [15], [14], [13].…”

Section: A Mapping Systemmentioning

confidence: 99%

Mapping with a ground robot in GPS denied and degraded environments

Rogers

Fink

Stump

2014

2014 American Control Conference

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A robot system operating in an unknown environment must be able to track its position to perform its mission. Vehicles with a consistent view of the sky, e.g., aerial or water surface platforms, can reliably make use of GPS signals to correct accumulated error from inertial measurements and feature-based mapping techniques. However, ground robots that must operate across a wide range of environments suffer from additional constraints which degrade the performance of GPS such as multipath and occlusion. In this paper, we present a methodology for incorporating GPS measurements into a feature-based mapping system for two purposes: providing georeferenced coordinates for high-level mission execution and correcting accumulated map error over long-term operation. We will present both the underlying system and experimental results from a variety of relevant environments such as military training facilities and large-scale mixed indoor and outdoor environments.

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Applying domain knowledge to SLAM using virtual measurements

Cited by 16 publications

References 15 publications

Planar surface SLAM with 3D and 2D sensors

Planar surface SLAM with 3D and 2D sensors

OmniMapper: A modular multimodal mapping framework

Mapping with a ground robot in GPS denied and degraded environments

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