A Probabilistic Approach to Collaborative Multi-Robot Localization

By Dieter Fox, Wolfram Burgard, H. Kruppa, Sebastian Thrun. 2000

828 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Electrical and Electronic Engineering

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 Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

Learning Multiagent Communication with Backpropagation (2016, background): “l for performing complex tasks in real-world environments where each actor has limited capabilities and/or visibility of the world. Practical examples include elevator control [4] and sensor networks [6]; communication is also important for success in robot soccer [27]. In any partially observed env”
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks (2016, background): “Such problems arise naturally in a variety of settings, such as multi-robot systems and sensor networks (Matari, 1997; Fox et al., 2000; Gerkey & Matari, 2004; Olfati-Saber et al., 2007; Cao et al., 2013).”
Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization (2005, background): “In the past, a variety of different particle-filter algorithms have been developed, and many variants have been applied with great success to various application domains [17], [27]–[33].”
A Survey and Analysis of Multi-Robot Coordination (2013, positive): “For example, multiple robots can localize themselves more efficiently if they exchange information about their position whenever they sense each other [25] [26] [27].”
Robotics-Based Location Sensing Using Wireless Ethernet (2002, background): “Multi-robot, collaborative localization is another branch of localization research [14].”
A vision-based formation control framework (2002, background): “Approaches to the multirobot localization problem involve estimating pose with respect to each other, the environment, or some combination thereof [1], [32], [33].”
The location stack: a layered model for location in ubiquitous computing (2002, background): “Finally, composite location systems and sensor fusion techniques are starting to enable multiplicity in the sensor technologies [8, 16].”
Coordinated multi-robot exploration (2005, background): “This allows the robots to discard those measurements which have been received by all other robots already.”
Location Systems for Ubiquitous Computing (2001, methodology): “The robots use Markov and Bayesian statistical techniques and multi-robot collaboration to accomplish sensor fusion [17].”
Multiagent learning is not the answer. It is the question (2007, neutral): “A partial list of other examples includes collaborative multi-robot localization [ 2 ], distributed network routing [4], distributed factory optimization [9], in-city driving [8], tracking teams of enemy combatants [13], and bidding in auctions [12].”
Swarm Robotic Behaviors and Current Applications (2020, background): “Fox et al. (2000) present a belief-based approach for collaborative multi-robot localization.”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “The bins can be implemented either as a fixed, multidimensional grid, or more compactly as tree structures [49, 61, 64, 75, 29].”
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots (2001, background): “It is well-known (Dellaert et al. 1999; Fox et al. 2000) that MCL—which our approach uses for posterior pose estimation—can solve the global localization problem.”