Information based adaptive robotic exploration

By Frédéric Bourgault, Alexei Makarenko, Stefan B. Williams, B. Grocholsky, H. F. Durrant-Whyte. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002

519 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/423567b410da7e8619c5ab361f88fa6ec58d82d0

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Sampling-based robotic information gathering algorithms (2014, background): “Modern applications of next best view planning and belief space planning continue to push the envelope of gradient-based optimization in these domains (Bourgault et al., 2002; van den Berg et al., 2012).”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, unknown): “theoretic function, as the information gain, could be used to rank the different actions [23], [233].”
Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, background): “One popular approach in the literature, described in several places (Whaite and Ferrie 1997; Feder et al. 1999; Bourgault et al. 2002), is gradient ascent in information gain.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, background): “However, soon after the first of these works emerged, it was observed that high uncertainty in the robot state estimation leads to wrong expected map uncertainties [27].”
Efficient Informative Sensing using Multiple Robots (2014, methodology): “Bourgault et al. (2002) proposed an exploration framework using an occupancy grid (OG) environment model (performing spatial decomposition of the observed environment) with an objective to maximize mutual information over the OG map. Stachniss et al. (2005) developed a greedy algorithm for selecting”
Global A-Optimal Robot Exploration in SLAM (2005, background): “There have also been a number of recent attempts to automate the exploration policy for gathering data to be assembled into a map; these algorithms have in particular focused on maximizing expected information gain [5], [6], [7] whenever possible.”
Identification and Representation of Homotopy Classes of Trajectories for Search-based Path Planning in 3D (2011, background): “For example, in order to deploy a group of agents to explore an environment [4], an efficient strategy ought to be able to identify the multiple homotopy classes and deploy at least one agent in each homotopy class.”
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters (2005, background): “As can be seen from this experiment, as soon as the robot gets too uncertain about its pose, it performs place re-visiting actions or chooses exploration actions which also reduce its pose uncertainty due to the information gathered along the path.”
An experiment in integrated exploration (2002, methodology): “The occupancy grid map [4] is maintained in parallel with the SLAM feature map.”