Global A-Optimal Robot Exploration in SLAM

By Robert Sim, Nicholas A. Roy. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

305 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2246a5ac64fa7607b5dea036f4c6e4793cf8e507

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Computer Networks and Communications

In Computer Vision and Pattern Recognition

Citations in the corpus

Near-optimal Observation Selection using Submodular Functions (2007, methodology): “Robotic Exploration. An important problem in robotics is Simultaneous Localization and Mapping (SLAM) (c.f., Sim & Roy (2005) ).”
Efficient Parallel Graph Exploration on Multi-Core CPU and GPU (2011, background): “Graphs are a fundamental data representation widely used in numerous fields such as intelligence analysis [2], robotics [3], social network analysis [4], and computational biology [5].”
Active Policy Learning for Robot Planning and Exploration under Uncertainty (2007, methodology): “An extension to continuous spaces (infinite number of bandits) using locally weighted regression was proposed in [17].”
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009, background): “Online path planning is essential for proper simultaneous localization and mapping (SLAM) [Sim and Roy (2005); Stachniss et al (2005)].”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “The latter was often disregarded in active SLAM because its traditional formulation did not allow for checking task completion and generated precision errors (det(Σ) → 0 rapidly when there are low-variance terms) [34], [140].”
Body schema acquisition through active learning (2010, methodology): “The new measurement y t is used to update the posterior of x .”
Active Policy Learning for Robot Planning and Exploration under Uncertainty (2008, background): “For instance, full observability is assumed in [9, 7], known robot location is assumed in [10], myopic planning is adopted in [8], and discretization of the state and/or actions spaces appears in [11, 12, 7].”
Efficient Informative Sensing using Multiple Robots (2014, methodology): “In contrast to such approaches, Sim and Roy (2005) attempted to optimize the entire trajectory, not just the next step, but their algorithm introduces some approximations without theoretical bounds.”
Optimal Value of Information in Graphical Models (2009, background): “In the robotics literature, Stachniss, Grisetti, and Burgard (2005), Sim and Roy (2005) and Kollar and Roy (2008) have presented approaches to information gathering in the context of Simultaneous Localization and Mapping (SLAM).”