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Planning to Perceive: Exploiting Mobility for Robust Object Detection
Published on Jul 21, 20113678 Views
Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest using a noisy object detector. The robot must reach its destination i
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Chapter list
Planning to Perceive: Exploiting Mobility for Robust Object Detection00:00
Motivation: Semantic Mapping - 100:09
Motivation: Semantic Mapping - 200:17
Motivation: Semantic Mapping - 300:18
Specific Problem02:02
Exploration Approaches02:39
Hybrid Approach03:51
The Cost of Detecting an Object04:43
Perception Field: Expected Information Around an Object Hypothesis05:34
Guided Forward Search06:26
Initial Algorithm06:56
The Result: Our Robot Stands Still - 107:20
The Result: Our Robot Stands Still - 207:50
Independence - 107:52
Independence - 208:03
Independence - 308:08
Independence - 408:14
A True Model for Object Detection - 108:39
A True Model for Object Detection - 208:55
A True Model for Object Detection - 308:56
A True Model for Object Detection - 409:03
A True Model for Object Detection - 509:07
Planing To Perceive: Correlated Observation History - 109:24
Planing To Perceive: Correlated Observation History - 209:50
Planing To Perceive: Correlated Observation History - 309:51
Planing To Perceive: Correlated Observation History - 409:56
Fully Correlated Input Model - 110:01
Fully Correlated Input Model - 210:14
Fully Correlated Input Model - 310:23
Dynamic Perception Field - 110:49
Dynamic Perception Field - 211:05
Dynamic Perception Field - 311:13
Planning To Perceive11:27
Object Detector and Perception Field - 111:53
Object Detector and Perception Field - 212:16
Object Detector and Perception Field - 312:41
Simulation13:20
Sample Trajectories13:46
Results: Simulation of 50 Runs - 114:31
Results: Simulation of 50 Runs - 214:41
Results: Simulation of 50 Runs - 315:08
Results: Simulation of 50 Runs - 415:35
Results: Simulation of 50 Runs - 515:53
Results: Simulation of 50 Runs - 616:58
Results: Simulation of 50 Runs - 717:05
Results: Simulation of 50 Runs - 817:12
Results: Simulation of 50 Runs - 917:21
Results: Real World17:51
Conclusion18:24