DP-SLAM: Fast, Robust Simultaneous Localization and Mapping Without Predetermined Landmarks

By Austin I. Eliazar, Ronald E. Parr. International Joint Conference on Artificial Intelligence, 2009

396 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7889415d727777af8e6ffc4fa93101f405ae0188

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Citations in the corpus

Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, methodology): “FastSLAM is an efficient implementation of particle filtering Pros: effective for loop closure, performs full and online SLAM, logarithmic complexity in number of features, no need for parametrization Cons: quality of the estimation is dependent on the number of particles DP-SLAM (Eliazar and Parr, ”
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters (2007, methodology): “To learn accurate grid maps, RBPFs have been used by Eliazar and Parr [3] and Hähnel et al. [5].”
Planning algorithms (2006, background): “There is a vast body of literature on probabilistic methods for mapping and localization, much of which is referred to as SLAM [942]; see also [184, 223, 277, 720, 774, 982].”
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.”
Map-Based Precision Vehicle Localization in Urban Environments (2007, background): “For an autonomous robot to stay in a lane, the localization requirements are in the order of decimeters.”
Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling (2005, methodology): “In this way the last reading is taken into account while generating the new particle, allowing to estimate the evolution of the system according to a more informed (and thus more accurate) model than the one obtained using only the last odometry reading as in [5] and [8].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, unknown): “Variations of PFs have then appeared, like the Distributed Particle approaches DP-SLAM [47] and DP-SLAM 2.0 [48] which proposed to use a minimal ancestry tree as a data storage structure.”
Distributed Multirobot Exploration and Mapping (2005, methodology): “Similar to the application of Rao-Blackwellised particle filters for mobile robot mapping [23], [13], [5], one can consider a particle filter as recursively computing posterior probability distributions over robot trajectories.”
Incremental smoothing and mapping (2008, background): “It can also be achieved in the context of particle-based representations [69, 98, 108, 38], where each particle carries its own correspondences, typically based on a per-particle maximum likelihood assignment or random sampling.”