Object disappearance for object discovery

A useful capability for a mobile robot is the ability to recognize the objects in its environment that move and change (as distinct from background objects, which are largely stationary). This ability can improve the accuracy and reliability of localization and mapping, enhance the ability of the robot to interact with its environment, and facilitate applications such as inventory management and theft detection. Rather than viewing this task as a difficult application of object recognition methods from computer vision, this work is in line with a recent trend in the community towards unsupervised object discovery and tracking that exploits the fundamentally temporal nature of the data acquired by a robot. Unlike earlier approaches, which relied heavily upon computationally intensive techniques from mapping and computer vision, our approach combines visual features and RGB-D data in a simple and effective way to segment objects from robot sensory data. We then use a Dirichlet process to cluster and recognize objects. The performance of our approach is demonstrated in several test domains.

Authors

• Julian Mason
• Bhaskara Marthi
• Ronald Parr

Keywords

• Image segmentation
• Visualization
• Robot sensing systems
• Feature extraction
• Image color analysis
• Clustering algorithms
• Computer Vision
• Visual Features
• Mobile Robot
• Inventory Management
• Dirichlet Process
• RGB-D Data
• Posterior Probability
• Markov Chain Monte Carlo
• Object Detection
• Precision And Recall
• Mixture Components
• Feature Clustering
• Depth Images
• Depth Data
• Current Frame
• Bag-of-words
• Multinomial Distribution
• Sensor Readings
• Dirichlet Distribution
• Set Of Segments
• Sparse Map
• Object Appearance
• Direct Quantitative Comparison
• Unique Object
• Well-studied Problem
• Dirichlet Process Mixture
• Gibbs Sampling
• 3D Space
• Depth Camera
• Automatic Segmentation