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Adaptive Intelligent Mobile Robots

Explore the development of a mobile vision system that can determine its location and identify objects by incorporating contextual clues. This project delves into scene analysis, object recognition, and place identification using state-of-the-art techniques like Hidden Markov Models and object priming. The system aims to predict object properties, locations, and categories based on contextual information, improving recognition accuracy and enabling seamless integration of local and global features. Future work includes enhancing object detection/localization with discriminative feature selection and scaling up place recognition to larger environments. Other related projects in the pipeline involve automatic topological map building, hierarchical POMDPs for localization, hierarchical abstraction for factored MDPs, and learning object segmentation from video data.

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Adaptive Intelligent Mobile Robots

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  1. Adaptive Intelligent Mobile Robots Kevin Murphy PI: Leslie Pack Kaelbling Artificial Intelligence Laboratory MIT

  2. Outline • Towards a mobile vision system that knows where it is & what it is looking at • Brief overview of other projects

  3. Context-based vision system for place and object recognition Antonio Torralba Kevin Murphy Bill Freeman Mark Rubin Submitted to ICCV ‘03

  4. Object out of context

  5. Object in context

  6. What is context? • What kind of location? (indoors/outdoors, office/corridor) • Which location?(Kevin’s office, Leslie’s office) • Viewing direction (facing the window) • Global scene factors (illumination) - Current activity (moving, sitting, talking)

  7. Wearable test-bed

  8. System diagram

  9. Computing the features

  10. Low-dimensional representation for scenes Compute image intensity (no color) Pipe image through steerable filter bank (6 orientations, 4 scales) Compute magnitude of response Downsample to 4 x 4 PCA to 80 dimensions

  11. Visualizing the filter bank output Images 80-dimensional representation

  12. Place recognition system

  13. Hidden Markov Model • Hidden states = location (63 values) • Observations = vGt2 R80 • Transition model encodes topology of environment • Observation model is a mixture of Gaussians (100 views per place)

  14. Place recognition demo

  15. Performance on known env. Ground truth System estimate Specific location Location category Indoor/outdoor

  16. Performance on new env.

  17. Comparison of features Categorization Recognition

  18. Effect of HMM on recognition Without With

  19. From place to object recognition

  20. Object priming • Predict object properties based oncontext (top-down signals): • Visual gist, vtG • Specific Location, Qt • Kind of location, Ct • Assume objects are independent conditional on context:

  21. Predicting object presence

  22. ROC curves for object detection based on context alone

  23. Predicting object position and scale

  24. Predicted segmentation

  25. Closing the loop Integrate local features (bottom up likelihood) with global features (top down prior)

  26. Future work • Add local features (bottom-up signal) for object detection/ localization • Model dependencies between objects • Scale-up place recognition to campus • Discriminative feature selection • Use a head tracker (view angle) • Recognize movemes (motion clips) • Online, unsupervised map and object class learning

  27. Some other projects • Automatic topological map building – Temizer • Hierarchical POMDPs for multi-scale localization – Theocharous & Murphy • Hierarchical abstraction for factored MDPs – Steinkraus • Learning object segmentation from video – Ross

  28. Automatic topological map building • Previous system did offline learning of topological map from labeled data • Goal: do online, unsupervised learning • “Rooms” (states) are regions for which local visual navigation suffices

  29. States Hierarchical POMDPs • Hierarchical model supports more efficient learning, inference (state estimation), and planning 600 states Vertical transitions horizontal transitions 1200 states

  30. Hierarchical abstraction for factored MDPs • Decompose domain using different abstractions • Dynamically adjust levels of abstraction based on current state and goal • Make decisions at highest possible level perception action current planning problem

  31. Learning object segmentation from video data • Videos contain moving objects, which are easy to segment from background. • Goal: learn model (MRF) to infer object boundaries in static images.

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