## Monte Carlo Localization: Efficient Position Estimation for Mobile Robots (1999)

### Cached

### Download Links

Venue: | IN PROC. OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI |

Citations: | 296 - 51 self |

### BibTeX

@INPROCEEDINGS{Fox99montecarlo,

author = {Dieter Fox and Wolfram Burgard and Frank Dellaert and Sebastian Thrun},

title = {Monte Carlo Localization: Efficient Position Estimation for Mobile Robots },

booktitle = {IN PROC. OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI},

year = {1999},

pages = {343--349},

publisher = {}

}

### Years of Citing Articles

### OpenURL

### Abstract

This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation " where needed." The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement...

### Citations

2410 |
A New Approach to Linear Filtering and Prediction Problems
- Kalman
- 1960
(Show Context)
Citation Context ...ot localization can be distinguished by the way they represent the state space of the robot. Kalman filter-based techniques. Most of the earlier approaches to robot localization apply Kalman filters (=-=Kalman 1960-=-). The vast majority of these approaches is based on the assumption that the uncertainty in the robot's position can be represented by a unimodal Gaussian distribution. Sensor readings, too, are assum... |

1165 | A novel approach to nonlinear and non-Gaussian Bayesian state estimation - Gordon, Salmond, et al. - 1993 |

831 |
Applied Optimal Estimation
- Gelb, editor
- 1974
(Show Context)
Citation Context ...stribution over possible positions, and use Bayes rule and convolution to update the belief whenever the robot senses or moves. The idea of probabilistic state estimation goes back to Kalman filters (=-=Gelb 1974-=-; Smith, Self, & Cheeseman 1990), which use multivariate Gaussians to represent the robot's belief. Because of the restrictive nature of Gaussians (they can basically represent one hypothesis only ann... |

664 | Robust Monte Carlo localization for mobile robots
- Thrun, Fox, et al.
(Show Context)
Citation Context ... because the computational overhead makes it impossible to incorporate sufficiently many images. MCL, however, succeeded in globally localizing the robot, and tracking the robot's position (see also (=-=Dellaert et al. 1999-=-a)). Figure 9 shows the path estimated by our MCL technique. Although the localization error is sometimes above 1 meter, the system is able to keep track of multiple hypotheses and thus to recover fro... |

520 |
An analysis of timedependent planning
- Dean, Boddy
- 1998
(Show Context)
Citation Context ...n to likelihood, MCL focuses its computational resources on regions with high likelihood, where things really matter. MCL is an online algorithm. It lends itself nicely to an any-time implementation (=-=Dean & Boddy 1988-=-; Zilberstein & Russell 1995). Any-time algorithms can generate an answer at any time; however, the quality of the solution increases over time. The sampling step in MCL can be terminated at any time.... |

432 |
Carlo filter and smoother for non-Gaussian non-linear state space models
- Kitagawa, ”Monte
- 1996
(Show Context)
Citation Context ...on (in short: MCL). Monte Carlo methods were introduced in the Seventies (Handschin 1970), and recently rediscovered independently in the target-tracking (Gordon, Salmond, & Smith 1993), statistical (=-=Kitagawa 1996-=-) and computer vision literature (Isard & Blake 1998), and they have also be applied in dynamic probabilistic networks (Kanazawa, Koller, & Russell 1995). MCL uses fast sampling techniques to represen... |

428 | A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning and Autonomous Robots (joint issue - Thrun, Burgard, et al. - 1998 |

414 | Estimating uncertain spatial relationships in robotics - Smith, Self, et al. - 1990 |

370 |
Condensation—conditional density propagation for visual tracking
- Isard, Blake
- 1998
(Show Context)
Citation Context ...troduced in the Seventies (Handschin 1970), and recently rediscovered independently in the target-tracking (Gordon, Salmond, & Smith 1993), statistical (Kitagawa 1996) and computer vision literature (=-=Isard & Blake 1998-=-), and they have also be applied in dynamic probabilistic networks (Kanazawa, Koller, & Russell 1995). MCL uses fast sampling techniques to represent the robot's belief. When the robot moves or senses... |

264 | Probabilistic robot navigation in partially observable environments
- Simmons, Koenig
- 1995
(Show Context)
Citation Context ...ntly several researchers have developed what is now a highly successful family of approaches capable of solving both localization problems: Markov localizations(Nourbakhsh, Powers, & Birchfield 1995; =-=Simmons & Koenig 1995-=-; Kaelbling, Cassandra, & Kurien 1996; Burgard et al. 1996). The central idea of Markov localization is to represent the robot's belief by a probability distribution over possible positions, and use B... |

258 | Directed sonar sensing for mobile robot navigation. Cambrage
- Leonard, Durrant-Whyte
- 1992
(Show Context)
Citation Context ... position. For these assumptions, Kalman filters provide extremely efficient update rules that can be shown to be optimal (relative to the assumptions) (Maybeck 1979). Kalman filter-based techniques (=-=Leonard & Durrant-Whyte 1992-=-; Schiele & Crowley 1994; Gutmann & Schlegel 1996) have proven to be robust and accurate for keeping track of the robot's position. However, since these techniques do not represent multi-modal probabi... |

224 | On sequential simulation-based methods for bayesian filtering
- Doucet, Godsill
- 1998
(Show Context)
Citation Context ... 1998), or the survival of the fittest algorithm (Kanazawa, Koller, & Russell 1995). All these methods are generically known as particle filters, and a discussion of their properties can be found in (=-=Doucet 1998-=-). The key idea underlying all this work is to represent the posterior belief Bel(l) by a set of N weighted, random samples or particles S = fs i j i = 1::Ng. A sample set constitutes a discrete appro... |

204 | The Interactive Museum Tour-Guide Robot
- Burgard, Cremers, et al.
- 1998
(Show Context)
Citation Context ... referred to as the hijacked robot problem (Engelson 1994)). The ability to localize itself---both locally and globally---played an important role in a collection of recent mobile robot applications (=-=Burgard et al. 1998-=-a; Endres, Feiten, & Lawitzky 1998; Kortenkamp, Bonasso, & Murphy 1997). While the majority of early work focused on the tracking problem, recently several researchers have developed what is now a hig... |

191 |
An Experiment in Guidance and Navigation of an Autonomous Robot Vehicle
- Cox, Blanche
- 1991
(Show Context)
Citation Context ... when compared to previous approaches. It is also much easier to implement. Introduction Throughout the last decade, sensor-based localization has been recognized as a key problem in mobile robotics (=-=Cox 1991-=-; Borenstein, Everett, & Feng 1996). Localization is a version of on-line temporal state estimation, where a mobile robot seeks to estimate its position in a global coordinate frame. The localization ... |

190 | Acting under uncertainty: discrete Bayesian models for mobile-robot navigation - Cassandra, Kaelbling, et al. |

185 |
Tools for statistical inference
- TANNER
- 1996
(Show Context)
Citation Context ...guity due to symmetry. Fig. 4: Successful localization. Properties of MCL A nice property of the MCL algorithm is that it can universally approximate arbitrary probability distributions. As shown in (=-=Tanner 1993-=-), the variance of the importance sampler converges to zero at a rate of 1= p N (under conditions that are true for MCL). The sample set size naturally trades off accuracy and computational load. The ... |

182 |
Markov Chains with Stationary Transition Probabilities
- Chung
- 1960
(Show Context)
Citation Context ...ng the probability that a measured movement action a, when executed at l 0 , carries the robot to l. Bel(l) is then updated according to the following general formula, commonly used in Markov chains (=-=Chung 1960-=-): Bel(l) /\Gamma Z P (l j l 0 ; a) Bel(l 0 ) dl 0 (1) The term P (l j l 0 ; a) represents a model of the robot's kinematics, whose probabilistic component accounts for errors in odometry. Following (... |

181 |
Stochastic Models
- MAYBECK
- 1979
(Show Context)
Citation Context ... Gaussian-shaped distributions over the robot's position. For these assumptions, Kalman filters provide extremely efficient update rules that can be shown to be optimal (relative to the assumptions) (=-=Maybeck 1979-=-). Kalman filter-based techniques (Leonard & Durrant-Whyte 1992; Schiele & Crowley 1994; Gutmann & Schlegel 1996) have proven to be robust and accurate for keeping track of the robot's position. Howev... |

178 | Estimating the absolute position of a mobile robot using position probability grids
- Burgard, Fox, et al.
- 1996
(Show Context)
Citation Context ... successful family of approaches capable of solving both localization problems: Markov localizations(Nourbakhsh, Powers, & Birchfield 1995; Simmons & Koenig 1995; Kaelbling, Cassandra, & Kurien 1996; =-=Burgard et al. 1996-=-). The central idea of Markov localization is to represent the robot's belief by a probability distribution over possible positions, and use Bayes rule and convolution to update the belief whenever th... |

177 | An improved particle filter for non-linear problems - Carpenter, Clifford, et al. - 1999 |

171 | DERVISH an office-navigating robot - Nourbakhsh, Powers, et al. - 1995 |

166 | An experimental comparison of localization methods
- Gutmann, Burgard, et al.
- 2002
(Show Context)
Citation Context ... the exact same data has already been used to compare different localization approaches, including grid-based Markov localization (which was the only one that solved the global localization problem) (=-=Gutmann et al. 1998-=-). Notice that the results for grid-based localization shown in Figure 6 were not generated in real-time. As shown there, the accuracy increases with the resolution of the grid, both for sonar (solid ... |

166 |
Using the SIR algorithm to simulate posterior distributions
- Rubin
- 1988
(Show Context)
Citation Context ...pplied in dynamic probabilistic networks (Kanazawa, Koller, & Russell 1995). MCL uses fast sampling techniques to represent the robot's belief. When the robot moves or senses, importance re-sampling (=-=Rubin 1988-=-) is applied to estimate the posterior distribution. An adaptive sampling scheme (Koller & Fratkina 1998), which determines the number of samples on-the-fly, is employed to trade-off computation and a... |

155 | Stochastic simulation algorithms for dynamic probabilistic networks - Kanazawa, Koller, et al. - 1995 |

132 | Navigating Mobile Robots: Systems and Techniques - Borenstein, Everett, et al. - 1996 |

122 | Using the condensation algorithm for robust, vision-based mobile robot localization
- Dellaert, Burgard, et al.
- 1999
(Show Context)
Citation Context ... because the computational overhead makes it impossible to incorporate sufficiently many images. MCL, however, succeeded in globally localizing the robot, and tracking the robot's position (see also (=-=Dellaert et al. 1999-=-a)). Figure 9 shows the path estimated by our MCL technique. Although the localization error is sometimes above 1 meter, the system is able to keep track of multiple hypotheses and thus to recover fro... |

110 |
MINERVA: A second generation mobile tourguide robot
- Thrun, Bennewitz, et al.
- 1999
(Show Context)
Citation Context ...st MCL in extreme situations, we evaluated it in a populated public place. During a two-week exhibition, our robot Minerva was employed as a tour-guide in the Smithsonian's Museum of Natural History (=-=Thrun et al. 1999-=-). To aid localization, Minerva is equipped with a camera pointed towards the ceiling. Figure 7 shows a mosaic of the museum's ceiling, constructed using a method described in (Thrun et al. 1999). The... |

104 | Comparison of position estimation techniques using occupancy grids
- Schiele, Crowley
- 1994
(Show Context)
Citation Context ...ns, Kalman filters provide extremely efficient update rules that can be shown to be optimal (relative to the assumptions) (Maybeck 1979). Kalman filter-based techniques (Leonard & Durrant-Whyte 1992; =-=Schiele & Crowley 1994-=-; Gutmann & Schlegel 1996) have proven to be robust and accurate for keeping track of the robot's position. However, since these techniques do not represent multi-modal probability distributions, whic... |

93 | Acting under uncertainty: Discrete bayesian models for mobile-robot navigation - Kaelbling, Cassandra, et al. - 1996 |

81 |
Markov Localization: A Probabilistic Framework for Mobile Robot Localization and Navigation, Doctoral thesis
- Fox
- 1998
(Show Context)
Citation Context ...lization---which has recently been applied with great success at various sites (Nourbakhsh, Powers, & Birchfield 1995; Simmons & Koenig 1995; Kaelbling, Cassandra, & Kurien 1996; Burgard et al. 1996; =-=Fox 1998-=-)---is to compute a probability distribution over all possible positions in the environment. Let l = hx; y; `i denote a position in the state space of the robot, where x and y are the robot's coordina... |

68 | Integrating Global Position Estimation and Position Tracking for Mobile Robots: The Dynamic Markov Localisation Approach
- Burgard, Derr, et al.
- 1998
(Show Context)
Citation Context ... referred to as the hijacked robot problem (Engelson 1994)). The ability to localize itself---both locally and globally---played an important role in a collection of recent mobile robot applications (=-=Burgard et al. 1998-=-a; Endres, Feiten, & Lawitzky 1998; Kortenkamp, Bonasso, & Murphy 1997). While the majority of early work focused on the tracking problem, recently several researchers have developed what is now a hig... |

67 | Amos: Comparison of scan matching approaches for self-localizati on in indoor environments
- Gutmann, Schlegel
- 1996
(Show Context)
Citation Context ...de extremely efficient update rules that can be shown to be optimal (relative to the assumptions) (Maybeck 1979). Kalman filter-based techniques (Leonard & Durrant-Whyte 1992; Schiele & Crowley 1994; =-=Gutmann & Schlegel 1996-=-) have proven to be robust and accurate for keeping track of the robot’s position. However, since these techniques do not represent multi-modal probability distributions, which frequently occur during... |

64 |
Active markov localization for mobile robots. Robotics and Autonomous Systems
- Fox, Burgard, et al.
- 1998
(Show Context)
Citation Context ...lysis), all evaluations have been performed strictly under run-time conditions (unless explicitly noted). In fact, we have routinely ran cooperative teams of mobile robots using MCL for localization (=-=Fox et al. 1999-=-). Comparison to Grid-Based Localization The first series of experiments characterizes the different capabilities of MCL and compares it to grid-based Markov localization, which presumably is the most... |

59 | AIbased Mobile Robots: Case studies of successful robot systems - Kortenkamp, Bonasso, et al. - 1998 |

56 | Using learning for approximation in stochastic processes
- Koeller, Fratkina
- 1998
(Show Context)
Citation Context ...ampling techniques to represent the robot's belief. When the robot moves or senses, importance re-sampling (Rubin 1988) is applied to estimate the posterior distribution. An adaptive sampling scheme (=-=Koller & Fratkina 1998-=-), which determines the number of samples on-the-fly, is employed to trade-off computation and accuracy. As a result, MCL uses many samples during global localization when they are most needed, wherea... |

55 | Position Estimation for Mobile Robots in Dynamic Environments
- Fox, Burgard, et al.
- 1998
(Show Context)
Citation Context ...rs have resorted to coarse-grained topological representations, whose granularity is often an order of magnitude lower than that of the grid-based approach. When high resolution is needed (see e.g., (=-=Fox et al. 1998-=-), who uses localization to avoid collisions with static obstacles that cannot be detected by sensors), such approaches are inapplicable. In this paper we present Monte Carlo Localization (in short: M... |

41 |
Comparison of Scan Matching approaches for self-Localization in indoor Environments,” EUROBOT
- Gutmann, Schlege, et al.
- 1996
(Show Context)
Citation Context ...de extremely efficient update rules that can be shown to be optimal (relative to the assumptions) (Maybeck 1979). Kalman filter-based techniques (Leonard & Durrant-Whyte 1992; Schiele & Crowley 1994; =-=Gutmann & Schlegel 1996-=-) have proven to be robust and accurate for keeping track of the robot's position. However, since these techniques do not represent multi-modal probability distributions, which frequently occur during... |

40 |
Monte Carlo techniques for prediction and filtering of non-linear stochastic processes
- Handschin
- 1970
(Show Context)
Citation Context ...stacles that cannot be detected by sensors), such approaches are inapplicable. In this paper we present Monte Carlo Localization (in short: MCL). Monte Carlo methods were introduced in the Seventies (=-=Handschin 1970-=-), and recently rediscovered independently in the target-tracking (Gordon, Salmond, & Smith 1993), statistical (Kitagawa 1996) and computer vision literature (Isard & Blake 1998), and they have also b... |

39 | Field test of a navigation system: Autonomous cleaning in supermarkets - Endres, Feiten, et al. - 1998 |

34 |
Passive Map Learning and Visual Place Recognition
- Engelson
- 1994
(Show Context)
Citation Context ...old its initial position; hence, it has to solve a much more difficult localization problem, that of estimating its position from scratch (this is sometimes referred to as the hijacked robot problem (=-=Engelson 1994-=-)). The ability to localize itself---both locally and globally---played an important role in a collection of recent mobile robot applications (Burgard et al. 1998a; Endres, Feiten, & Lawitzky 1998; Ko... |

10 | A monte carlo algorithm for multi-robot localization
- Fox, Burgard, et al.
- 1999
(Show Context)
Citation Context ...lysis), all evaluations have been performed strictly under run-time conditions (unless explicitly noted). In fact, we have routinely ran cooperative teams of mobile robots using MCL for localization (=-=Fox et al. 1999-=-). Comparison to Grid-Based Localization The first series of experiments characterizes the different capabilities of MCL and compares it to grid-based Markov localization, which presumably is the most... |