GATMO: a generalized approach to tracking movable objects

Authors:
Garratt Gallagher;Siddhartha S. Srinivasa;J. Andrew Bagnell;Dave Ferguson
Affiliations:
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Intel Research Pittsburgh, Pittsburgh, PA;Robotics Institute, Carnegie Mellon University, Pittsburgh, PA;Intel Research Pittsburgh, Pittsburgh, PA
Venue:
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Year:
2009

Citing 7
Cited 4

Robust Monte Carlo localization for mobile robots

Artificial Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Navigation among movable obstacles

Navigation among movable obstacles
Mobile robot mapping and localization in non-static environments

AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Relational object maps for mobile robots

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning hierarchical object maps of non-stationary environments with mobile robots

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Semantic Mapping Using Mobile Robots

IEEE Transactions on Robotics

Mapping with sparse local sensors and strong hierarchical priors

TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Towards hierarchical blackboard mapping on a whiskered robot

Robotics and Autonomous Systems
Lifelong localization in changing environments

International Journal of Robotics Research
Model-based autonomous system for performing dexterous, human-level manipulation tasks

Autonomous Robots

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present GATMO (Generalized Approach to Tracking Movable Objects), a system for localization and mapping that incorporates the dynamic nature of the environment while maintaining semantic labels. Objects in the environment are broken down into multiple mobility levels, from static (walls) to highly mobile (people), by maintaining a history of object movement. Object classification is accomplished through a multi-layer, multi-hypothesis approach that does not rely on any static features such as shape or size. Maps are stored in an efficient manner that incorporates a history of previous orientations of each object. GATMO is initialized with a static map; it subsequently changes the map over time as objects in the map change position.