21,516 research outputs found
Next Best View Planning for Object Recognition in Mobile Robotics
Recognising objects in everyday human environments is a challenging task for autonomous mobile robots. However, actively planning the views from which an object might be perceived can significantly improve the overall task performance. In this paper we have designed, developed, and evaluated an approach for next best view planning. Our view planning approach is based on online aspect graphs and selects the next best view after having identified an initial object candidate. The approach has two steps. First, we analyse the visibility of the object candidate from a set of candidate views that are reachable by a robot. Secondly, we analyse the visibility of object features by projecting the model of the most likely object into the scene. Experimental results on a mobile robot platform show that our approach is (I) effective at finding a next view that leads to recognition of an object in 82.5% of cases, (II) able to account for visual occlusions in 85% of the trials, and (III) able to disambiguate between objects that share a similar set of features. Hence, overall, we believe that the proposed approach can provide a general methodology that is applicable to a range of tasks beyond object recognition such as inspection, reconstruction, and task outcome classification
Active Classification: Theory and Application to Underwater Inspection
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.Comment: 16 page
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
- …