1 Am I Lost? Or Has the World Changed? Am I Lost? Or Has the World Changed? Persistent Autonomous Persistent Autonomous Navigation in Changing Navigation in Changing Environments Environments Aisha Walcott PhD Candidate, EECS, Robotics Advisor: Professor John Leonard [draft of algorithm and framework]
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Am I Lost? Or Has the World Changed? Persistent Autonomous Navigation in Changing Environments
Am I Lost? Or Has the World Changed? Persistent Autonomous Navigation in Changing Environments. Aisha Walcott PhD Candidate, EECS, Robotics Advisor: Professor John Leonard [draft of algorithm and framework]. SLAM in Changing Environments. - PowerPoint PPT Presentation
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Am I Lost? Or Has the World Changed?Am I Lost? Or Has the World Changed?Persistent Autonomous Navigation in Persistent Autonomous Navigation in
Changing Environments Changing Environments
Aisha Walcott
PhD Candidate, EECS, Robotics
Advisor: Professor John Leonard
[draft of algorithm and framework]
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SLAM in Changing EnvironmentsSLAM in Changing EnvironmentsGoal is to maintain an up-to-date map in a
changing environment
1: 1:( , | , )P X M , C Z UC t t t
Related Work [Montessano et al, 2005, Wang et. Al, 2007, Wolf, Sukhatme, 2005]
Change Indicator
1: 1: 1: 1
1: 1: 1 1: 1: 1
( | , , , )
( | , , ) ( | , )tP M X C Z U
P X C Z U P C Z Ut t t
C t t t t
Map is function of time
Subset of trajectory
1:X XC t
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Talk OutlineTalk Outline
Introduction to SLAM
Traditional SLAM vs. Life-long SLAM
Mobile Robot Model
Probabilistic Problem Formulation
Pose Graph Approach
Dynamic Pose Graph SLAM (DPG-SLAM)
Simulated Data Analysis
Conclusion
4
Pose Graph SLAMPose Graph SLAM
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Pose Graph SLAMPose Graph SLAM
Odometry
Scan Matching
Scan Matchingand
Loop Constraints
50m
6
Pose Graph FormulationPose Graph FormulationNetwork of spatial relations
Estimate relative geometric constraints between two poses
Translation in the x and y, and Rotation relative to pose
Odometry and scan matching (improved estimate)
x1
x7
Constraints
T1,2
T2,3
T3,4
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Pose Graph FormulationPose Graph FormulationNetwork of spatial relations
Estimate relative geometric constraints between two poses
Translation in the x and y, and Rotation relative to pose
Odometry and scan matching (improved estimate)
x1
x7
Constraints
T1,2
T2,3
T3,4
Ground Truth
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Pose Graph FormulationPose Graph FormulationNetwork of spatial relations
Estimate relative geometric constraints between two poses
Translation in the x and y, and rotation ω relative to pose
Odometry and scan matching (improved estimate)
x1
Constraints
T1,2
T2,3
Non-linear Motion Model
2, ,2
, , 12
, ( , )Tx x y x
i j y y i i i
Tx
Ty f x u w
Zero-mean Gaussian Noise
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Pose Graph FormulationPose Graph Formulation“Closing the loop”
and Mapping” [Kaess et al., 2008]Returns estimate of robot trajectoryPoses and covariances are given in global frameComputes best configuration of poses according to a cost
function
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Talk OutlineTalk Outline
Introduction to SLAM
Traditional SLAM vs. Life-long SLAM
Mobile Robot Model
Probabilistic Problem Formulation
Pose Graph Approach
Dynamic Pose Graph SLAM (DPG-SLAM)
Experiments and Results
Conclusion
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Dynamic Pose Graph SLAM (DPG SLAM)Dynamic Pose Graph SLAM (DPG SLAM)Objective
Environment Model
Assumptions
Challenges
DPG-SLAM Algorithm
Analysis
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ObjectiveObjectiveIf the world changes and ……Robot is certain about its position then not likely to be
lost
or
…Robot is not certain about its position then likely to become lost
Continuous map update and change detection to keep map current
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Environment ModelEnvironment ModelStatic entities S = {s1…sj} Semi-static entities E = {e1…ek}Dynamic entities D = {d1…dm} Env = S E D