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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Monte Carlo localization canonical | 4 |
How this entity was disambiguated
This entity first appeared as the object of triple T4650850 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo localization Context triple: [Probabilistic Robotics, topic, Monte Carlo localization]
-
A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
-
B.
CMU Highly Intelligent Mobile Platform
CMU Highly Intelligent Mobile Platform (CHIMP) is a sophisticated humanoid robot developed at Carnegie Mellon University for advanced mobility, manipulation, and autonomous operation in challenging environments.
-
C.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
D.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
E.
Technical Committee on Mobile Robots
The Technical Committee on Mobile Robots is a specialized IEEE Robotics and Automation Society group that advances research, standards, and collaboration in autonomous and mobile robotics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monte Carlo localization Target entity description: 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.
-
A.
SLAM
SLAM is a major art museum in St. Louis, Missouri, renowned for its extensive collection spanning thousands of years and diverse cultures.
-
B.
CMU Highly Intelligent Mobile Platform
CMU Highly Intelligent Mobile Platform (CHIMP) is a sophisticated humanoid robot developed at Carnegie Mellon University for advanced mobility, manipulation, and autonomous operation in challenging environments.
-
C.
book "Probabilistic Robotics"
"Probabilistic Robotics" is a foundational textbook that systematically introduces probabilistic methods for perception, localization, and control in mobile robotics.
-
D.
Learning to See by Moving
"Learning to See by Moving" is a research work in computer vision that explores how visual understanding can emerge from an agent’s own movement and interaction with the environment, rather than from static images alone.
-
E.
Technical Committee on Mobile Robots
The Technical Committee on Mobile Robots is a specialized IEEE Robotics and Automation Society group that advances research, standards, and collaboration in autonomous and mobile robotics.
- F. None of above. chosen
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 ⓘ
linked to:
Monte Carlo method
|
| estimates |
robot orientation
ⓘ
robot pose ⓘ robot position ⓘ |
| field |
mobile robotics
ⓘ
probabilistic robotics ⓘ
linked to:
book "Probabilistic Robotics"
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 ⓘ
linked to:
SLAM
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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Monte Carlo localization Description of subject: 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.
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.