Lecture 7: Localization Flashcards
What is Map-based localization?
The robot estimates its position using perceived information and a map
What about the 2 things of the map used in map-based localization?
It might be known.
It might be built in parallel(simultaneous localization and mapping-SLAM)
What are the 2 challenges of localization?
1)Measurements and the map are inherently error prone
2)The robot has to deal with uncertain information
What is the approach of Localization?
The robot estimates the belief state about its position through an SEE and ACT cycle
Measurements are error prone because of the following 3 things:
Odometry
Exteroceptive sensors(camera, laser)
Map
The first See involves what?
The robot queries its sensors -> finds itself next to a pillar
The Act involves what?
Robot moves one meter forward
* Motion estimated by wheel encoders
* Accumulation of uncertainty
The second See involves what?
The robot queries its sensors again -> finds itself next to a pillar
What does the belief update involve?
Information Fusion
What are the 4 types of maps?
-Continuous map with single hypothesis
probability distribution π(π₯)
-Continuous map with multiple hypotheses
probability distribution π(π₯)
-Discretized metric map (grid π) with
probability distribution π(π)
-Discretized topological map (nodes π) with
probability distribution π(π).
What does the Bayes rule relate and how is it written?
the conditional probability p(x|y) to its inverse p(y|x).
p(x|y) = np(y|x)p(x); n = p(y)^-1 normalization factor(integral of p = 1)
Who uses the Bayes Rule and where is the Bayes rule theorem used?
The theorem is used by both Markov and Kalman-filter localization algorithms during the measurement update.
Concerning application of theorem of total probability/convolution, what two probabilities is it applied to? What is the difference?
Continuous and Discrete probabilities. Continuous probabilities has dx(subscript)t-1
What is Markov localization for?
Discretized pose representation
What is Kalman filter for?
Continuous pose representation and Gaussian error model
What is the SLAM problem?
how can a body navigate in a previously unknown environment, while constantly building and updating a map of its workspace using onboard sensors & onboard computation
In what 4 situations is SLAM necessary?
-When a robot must be truly autonomous (no human input)
* When there is no prior knowledge about the environment
* When we cannot rely exclusively on pre-placed beacons or external
positioning systems (e.g., GPS)
* When the robot needs to know where it is
How do you track the motion of a robot while it is moving?
SLAM(Simultaneous Localization and Mapping)
SLAM is the backbone of what?
the spatial awareness of a robot
The spatial awareness of a robot is what?
one of the most challenging problems in probabilistic robotics.
An unbiased map is necessary for what?
Localizing the robot; pure localization with a known map
An accurate pose estimates is necessary for?
building a map of the environment; Mapping with known robot poses.
What makes SLAM better than an unbiased map and an accurate pose estimate?
SLAM has no prior knowledge of the robotβs workspace. The robot poses have to be estimated along the way.
Where does SLAM originate from?
efforts to formalize production of topographic maps from aerial imagery
