Dynamic sensor planning with stereo for model identification on a mobile platform

Ma, Jie; Burdick, Joel W.

doi:10.1109/robot.2010.5509205

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The problem of active multi-view recognition has been studied extensively for computer vision applications [15,5,13], including the use of depth maps in medical imagery [21]. Ma and Burdick [11] also provide a recent application of active planning for simultaneous pose estimation and recognition of a moving object using a mobile robot. While different forms of information gain play a critical role in these prior works, a key distinction in our work is the notion of adaptivity.…”

Section: Related Workmentioning

confidence: 99%

Active Classification: Theory and Application to Underwater Inspection

Hollinger

Mitra

Sukhatme

2016

Springer Tracts in Advanced Robotics

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We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We validate our approach in two ways, both related to underwater inspection: 3D polyhedra recognition in synthetic depth maps and ship hull inspection with imaging sonar. These tasks encompass both the planning and recognition aspects of the active classification problem. The results demonstrate that actively planning for informative views can reduce the number of necessary views by up to 80% when compared to passive methods.

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Section: Related Workmentioning

confidence: 99%

Active Classification: Theory and Application to Underwater Inspection

Hollinger

Mitra

Sukhatme

2016

Springer Tracts in Advanced Robotics

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“…They used Bayesian state estimation to update the current state probability distribution based on a scene observation which depends on the sensor parameters. Recently Ma and Burdick [21] proposed an information-based approach to active sensor planning. A stereo camera was mounted on a mobile platform and the robot estimated its pose simultaneously and identified a moving object without constraints on the motion.…”

Section: Related Workmentioning

confidence: 99%

Active view planing for human observation through a RGB-D camera

Sheng

2013

2013 IEEE Workshop on Robot Vision (WORV)

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Human sensing is always an important topic for robotic applications. In this paper, we proposed an active view planning approach for human observation on a mobile robot platform with sensor data processing. The sensor adopted in our research is an inexpensive RGB-D camera. A new measure based on distance and orientation information is introduced to evaluate the quality of the viewpoint when the robot detects the human subject. The result shows that the robot can move to the best viewpoint based on the proposed approach. 114 978-1-4673-5647-3/12/$31.00 ©2012 IEEE 114 978-1-4673-5648-0/13/$31.00 ©2013 IEEE

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“…Denzler and Brown [7] and Derichs et al [8] formulate their solutions for continuous domains, but only demonstrate their active object class identification methodology and neglect pose determination. Ma and Burdick [9] present a methodology for 6-DOF pose estimation and tracking to actively recognize moving objects. However, the relevant aspects of probabilistic modeling are not detailed.…”

Section: Related Workmentioning

confidence: 99%

Active perception and scene modeling by planning with probabilistic 6D object poses

Eidenberger

Scharinger

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents an approach to probabilistic active perception planning for scene modeling in cluttered and realistic environments. When dealing with complex, multiobject scenes with arbitrary object positions, the estimation of 6D poses including their expected uncertainties is essential. The scene model keeps track of the probabilistic object hypotheses over several sequencing sensing actions to represent the real object constellation.To improve detection results and to tackle occlusion problems a method for active planning is proposed which reasons about model and state transition uncertainties in continuous and highdimensional domains. Information theoretic quality criteria are used for sequential decision making to evaluate probability distributions. The probabilistic planner is realized as a partially observable Markov decision process (POMDP).The active perception system for autonomous service robots is evaluated in experiments in a kitchen environment. In 80 test runs the efficiency and satisfactory behavior of the proposed methodology is shown in comparison to a random and a stepaside action selection strategy. The objects are selected from a large database consisting of 100 different household items.

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Dynamic sensor planning with stereo for model identification on a mobile platform

Cited by 6 publications

References 14 publications

Active Classification: Theory and Application to Underwater Inspection

Active Classification: Theory and Application to Underwater Inspection

Active view planing for human observation through a RGB-D camera

Active perception and scene modeling by planning with probabilistic 6D object poses

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