Monte Carlo localization
E457844
Monte Carlo localization is a probabilistic robotics algorithm that uses particle filters to estimate a robot’s pose within a known map based on noisy sensor and motion data.
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
particle filter algorithm
ⓘ
probabilistic robotics method ⓘ robot localization algorithm ⓘ |
| advantage |
can recover from localization failures
ⓘ
can represent multi-modal distributions ⓘ robust to global localization uncertainty ⓘ |
| appliedIn |
autonomous service robots
ⓘ
autonomous vehicles ⓘ indoor mobile robots ⓘ warehouse robots ⓘ |
| approximates | posterior distribution over poses ⓘ |
| assumes | known map ⓘ |
| basedOn |
Markov localization
ⓘ
Monte Carlo methods NERFINISHED ⓘ |
| estimates |
robot orientation
ⓘ
robot pose ⓘ robot position ⓘ |
| field |
mobile robotics
ⓘ
probabilistic robotics NERFINISHED ⓘ robotics ⓘ |
| handles |
noisy motion data
ⓘ
noisy sensor data ⓘ |
| hasStep |
prediction step
ⓘ
resampling step ⓘ update step ⓘ |
| input |
camera observations
ⓘ
laser scanner data ⓘ odometry measurements ⓘ range sensor measurements ⓘ sonar data ⓘ |
| output |
most likely pose estimate
ⓘ
pose probability distribution ⓘ |
| relatedTo |
Kalman filter localization
ⓘ
Simultaneous Localization and Mapping NERFINISHED ⓘ grid-based Markov localization ⓘ |
| represents |
belief distribution with particles
ⓘ
belief over robot pose ⓘ |
| requires | sufficient number of particles ⓘ |
| tradeOff | accuracy versus computational cost ⓘ |
| typicalMapRepresentation |
feature-based map
ⓘ
occupancy grid map ⓘ |
| uses |
Bayesian filtering
ⓘ
importance sampling ⓘ motion model ⓘ particle filter ⓘ resampling ⓘ sensor model ⓘ |
| variant |
adaptive Monte Carlo localization
ⓘ
global Monte Carlo localization ⓘ |
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
subject surface form:
Probabilistic Robotics