EE565:Mobile Robotics Lecture 9 - Lahore University of...
Transcript of EE565:Mobile Robotics Lecture 9 - Lahore University of...
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EE565:Mobile Robotics Lecture 9
Welcome
Dr. Ahmad Kamal Nasir
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EE565: Mobile Robotics Module 4: Localization and Mapping
Probabilistic Map-based Localization
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EE565: Mobile Robotics Module 4: Localization and Mapping
Definition, Challenges and Approach
• Map-based localization – The robot estimates its position using perceived information and a
map – The map
• might be known (localization) • Might be built in parallel (simultaneous localization and mapping - SLAM)
• Challenges – Measurements and the map are inherently
error prone – Thus the robot has to deal with uncertain
information • Probabilistic map-base localization
• Approach – The robot estimates the belief state about its
position through an ACT and SEE cycle
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
SEE and ACT to improve belief state
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EE565: Mobile Robotics Module 4: Localization and Mapping
Using Motion Model and its Uncertainties
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EE565: Mobile Robotics Module 4: Localization and Mapping
Estimation of Position based on Perception and Map
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EE565: Mobile Robotics Module 4: Localization and Mapping
Fusion of Prior Belief with Observation
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EE565: Mobile Robotics Module 4: Localization and Mapping
The Estimation Cycle (ACT-SEE)
• Information (measurements) is error prone (uncertain) – Odometry
– Exteroceptive sensors (camera, laser, …)
– Map
• Probabilistic map-based localization
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EE565: Mobile Robotics Module 4: Localization and Mapping
Belief Representation
• Continuous map with single hypothesis probability distribution 𝑝 𝑥
• Continuous map with multiple hypotheses probability distribution 𝑝 𝑥
• Discretized metric map (grid 𝑘) with probability distribution 𝑝 𝑘
• d) Discretized topological map (nodes 𝑛) with
• probability distribution 𝑝 𝑛
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EE565: Mobile Robotics Module 4: Localization and Mapping
ACT - SEE Cycle for Localization
• SEE: The robot queries its sensors – finds itself next to a pillar
• ACT: Robot moves one meter forward – motion estimated by wheel
encoders – accumulation of uncertainty
• SEE: The robot queries its sensors – again → finds itself next to a pillar
• Belief update (information fusion)
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EE565: Mobile Robotics Module 4: Localization and Mapping
Refresher on Probability Theory: joint distribution
• 𝑝 𝑥, 𝑦 : joint distribution representing the probability that the random variable 𝑋 takes on the value 𝑥 and that 𝑌 takes on the value𝑦
• If 𝑋 and 𝑌 are independent we can write:
𝑝 𝑥, 𝑦 = 𝑝 𝑥 𝑝 𝑦
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EE565: Mobile Robotics Module 4: Localization and Mapping
Refresher on Probability Theory: conditional probability
• 𝑝 𝑥 𝑦 : conditional probability that describes the probability that the random variable 𝑋 takes on the value 𝑥 conditioned on the knowledge that 𝑌 for sure takes 𝑦.
𝑝 𝑥 𝑦 =𝑝 𝑥, 𝑦
𝑝 𝑦
• and if 𝑋 and 𝑌 are independent (uncorrelated) we can write:
𝑝 𝑥 𝑦 =𝑝 𝑥 𝑝 𝑥
𝑝 𝑦= 𝑝 𝑥
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EE565: Mobile Robotics Module 4: Localization and Mapping
Refresher on Probability Theory: theorem of total probability
• The theorem of total probability (convolution) originates from the axioms of probability theory and is written as:
• This theorem is used by both Markov and Kalman-filter localization algorithms during the prediction update.
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EE565: Mobile Robotics Module 4: Localization and Mapping
Refresher on Probability Theory: the Bayes rule
• The Bayes rule relates the conditional probability 𝑝 𝑥 𝑦 to its inverse 𝑝 𝑦 𝑥
• Under the condition that 𝑝 𝑦 > 0, the Bayes rule is written as:
• This theorem is used by both Markov and Kalman-filter localization algorithms during the measurement update.
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EE565: Mobile Robotics Module 4: Localization and Mapping
Markov localization: applying probability theory to localization
• Information (measurements) is error prone (uncertain) – Odometry
– Exteroceptive sensors (camera, laser, …)
– Map
• Probabilistic map-based localization
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EE565: Mobile Robotics Module 4: Localization and Mapping
Basics and Assumption
• Discretized pose representation 𝑥𝑡→ grid map • Markov localization tracks the robot’s
belief state 𝑏𝑒𝑙 𝑥𝑡 using an arbitrary probability density function to represent the robot’s position
• Markov assumption: Formally, this means that the output of the estimation process is a function 𝑥𝑡 only of the robot’s previous state 𝑥𝑡−1 and its most recent actions (odometry) 𝑢𝑡 and perception 𝑧𝑡.
• Markov localization addresses the global localization problem, the position tracking problem, and the kidnapped robot problem.
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EE565: Mobile Robotics Module 4: Localization and Mapping
Applying Probability Theory to Localization
• ACT | probabilistic estimation of the robot’s new belief state 𝑏𝑒𝑙 𝑥𝑡 based on the previous location 𝑏𝑒𝑙 𝑥𝑡−1 and the probabilistic motion model 𝑝(𝑥𝑡|𝑢𝑡, 𝑥𝑡−1) with action 𝑢𝑡 (control input)
– application of theorem of total probability / convolution
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EE565: Mobile Robotics Module 4: Localization and Mapping
Applying Probability Theory to Localization
• SEE | probabilistic estimation of the robot’s new belief state 𝑏𝑒𝑙 𝑥𝑡 as a function of its measurement data 𝑧𝑡 and its former belief state 𝑏𝑒𝑙 𝑥𝑡 : – application of Bayes rule
• where 𝑝 𝑧𝑡 𝑥𝑡, 𝑚𝑡 is the probabilistic measurement model (SEE), that is, the probability of observing the measurement data 𝑧𝑡 given the knowledge of the map 𝑚𝑡 and the robot’s position 𝑥𝑡. Thereby 𝜂 = 𝑝 𝑦 −1is the normalization factor so that ∑𝑝 = 1.
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EE565: Mobile Robotics Module 4: Localization and Mapping
The Basic Algorithms for Markov Localization
• Markov assumption: Formally, this means that the output is a function 𝑥𝑡 only of the robot’s previous state 𝑥𝑡−1 and its most recent actions (odometry) 𝑢𝑡 and perception 𝑧𝑡.
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EE565: Mobile Robotics Module 4: Localization and Mapping
Using Motion Model and its Uncertainties
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EE565: Mobile Robotics Module 4: Localization and Mapping
Using Motion Model and its Uncertainties
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EE565: Mobile Robotics Module 4: Localization and Mapping
Estimation of Position based on Perception and Map
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EE565: Mobile Robotics Module 4: Localization and Mapping
Extension to 2D
• The real world for mobile robot is at least 2D (moving in the plane) – discretized pose state space (grid) consists of 𝑥, 𝑦, 𝜃 – Markov Localization scales badly with the size of
the environment
• Space: 10 m x 10 m with a grid size of 0.1 m and an angular resolution of 1° – 100 ⋅ 100 ⋅ 360 = 3.6 106 grid points (states) – prediction step requires in worst case 3.6 106 2
multiplications and summations
• Fine fixed decomposition grids result in a huge state space – Very important processing power needed – Large memory requirement
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EE565: Mobile Robotics Module 4: Localization and Mapping
Reducing Computational Complexity
• Adaptive cell decomposition
• Motion model (Odomety) limited to a small number of grid points
• Randomized sampling – Approximation of belief state by a representative subset of
possible locations
– weighting the sampling process with the probability values
– Injection of some randomized (not weighted) samples
– randomized sampling methods are also known as particle filter algorithms, condensation algorithms, and Monte Carlo algorithms.
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EE565: Mobile Robotics Module 4: Localization and Mapping
Kalman Filter Localization: applying probability theory to localization
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EE565: Mobile Robotics Module 4: Localization and Mapping
Basics and assumption
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EE565: Mobile Robotics Module 4: Localization and Mapping
prediction (odometry) - ACT
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EE565: Mobile Robotics Module 4: Localization and Mapping
Observation - SEE
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EE565: Mobile Robotics Module 4: Localization and Mapping
Observations in Sensor Model Space
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EE565: Mobile Robotics Module 4: Localization and Mapping
Measurement Prediction
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EE565: Mobile Robotics Module 4: Localization and Mapping
Measurement Prediction in Model Space
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EE565: Mobile Robotics Module 4: Localization and Mapping
Matching in Sensor Model Space
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EE565: Mobile Robotics Module 4: Localization and Mapping
Estimation
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EE565: Mobile Robotics Module 4: Localization and Mapping
Kalman Filter Localization in summery
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EE565: Mobile Robotics Module 4: Localization and Mapping
Questions
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