Active Markov localization for mobile robots

By Dieter Fox, Wolfram Burgard, Sebastian Thrun. 1998

How researchers see this paper

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Citations in the corpus

Learning World Graphs to Accelerate Hierarchical Reinforcement Learning (2019, positive): “a map of the world [61, 87, 3]. Our exploration strategy borrows the high-level insight from robotics active localization, where robots are actively guided to investigate unfamiliar regions by humans [29, 58]. Another direction in this area is to learn a world model [4, 37, 36] that generates latent”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, methodology): “[26], who also separate the procedure in three major stages as in [16].”
Robust Monte Carlo localization for mobile robots (2001, methodology): “In particular, we compared MCL to grid-based Markov localization, our previous best stochastic localization algorithm and one of the very few algorithms capable of localizing a robot globally [7,23].”
Active Neural Localization (2018, methodology): “We run AML with all possible combination of values of n l ∈ { 1 , 5 , 10 , 15 } , n g ∈ { 1 , n l } and n m = { 5 , 10 } and deﬁne two versions: (1) Active Markov Localization (Fast): Generalized AML algorithm using the values of n l , n g , n m that maximize the performance while keeping the runtim”
Trajectory Optimization using Reinforcement Learning for Map Exploration (2008, methodology): “Fox et al. (1998) use the same criterion of entropy minimization to find a trajectory that localizes the robot as quickly as possible.”
An experiment in integrated exploration (2002, background): “Active stochastic localization is implemented in [6].”
Reinforcement learning for sensing strategies (2004, methodology): “In this paper we illustrate and evaluate our approach to active sensing using the task of kicking a ball into the opponent’s goal.”
Using the CONDENSATION algorithm for robust, vision-based mobile robot localization (1999, background): “idea forms the basis of the grid-based Markov localization approach [5, 11 ].”
Probabilistic Algorithms in Robotics (2000, background): “They also include work on active localization [11, 30], where a robot moves to places that maximally disambiguate its pose.”
Finding Approximate POMDP solutions Through Belief Compression (2011, unknown): “The problem of measuring uncertainty with entropy and then acting to improve the localisation estimate is not a new one, and can be found in the active localisation literature ( Fox et al., 1998 ).”