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Master-Praktikum - Sensorgestützte intelligente Umgebungen (IN2106, IN8902, IN4053)

Dauer6 SWS
VortragendeZoltan Marton, Nico Blodow
Lehrzielsiehe Modulbeschreibung IN2106
Termin22.11.2011, 3PM
Midterm presentations January 13, 2011
Endterm presentations March 16, 2011

Project Ideas

  1. Interactive Segmentation (Dejan Pangercic)
    • segmentation of everyday objects in the cluttered scenes
    • inclusion of further cues such as color, 3D features, manipulator feedback
    • multi-cue classification
    • Research Areas: image segmentation, control theory, machine learning.
    • Tools: Linux, ROS, OpenCV, PCL
  2. CUDA-based 3D Image Processing for Object Recognition (Nico Blodow)
    • multiple-cue image segmentation and classification
    • combination of image and depth features to increase performance / robustness
    • Conditional Random Field (or similar) classifier
    • Research Areas: computer vision, machine learning, 3D processing
    • Tools: Linux, ROS, OpenCV, PCL (pcl::cuda)
  3. Ensemble Methods for Multi-Cue Object Classification (Zoltan-Csaba Marton)
    • combination of vision- and 3D-based features from the Kinect sensor
    • ensemble of specialized routines to improve object detection
    • building an object model database for storing information about objects
    • perception system for selecting the right methods to use for a given task
    • Research Areas: machine learning, cognitive vision, semantic mapping
    • Tools: Linux, ROS, your brain (and possibly PCL and OpenCV)
  4. Eye-in-Hand Modeling (Dejan Pangercic)
    • enabling the technology for eye-in-hand-based reconstruction of scenes
    • design of the multi-purpose tool holder
    • hand-eye calibration for the external tool using AR markers
    • simple scene reconstruction from multiple views
    • Research Areas: Image recognition, control theory, mapping
    • Tools: Linux, ROS, OpenCV, PCL, CNC Machine
  5. Hand-held Modeling of Indoor Scenes (Dejan Pangercic)
    • RGBD SLAM (simultaneous localization and mapping using RGB and depth cameras)
    • evaluate existing RGBD SLAM approaches (UW, Freiburg, CCNY, Willow Garage)
    • depending on previous findings improve or combine with IAS tools
    • scene reconstruction of various (actual) households
    • IKEA example with interior design assistance
    • Research Areas: SLAM, mapping, surface reconstruction, computer graphics
    • Tools: Linux, ROS, OpenCV, PCL.
  6. Semantic Labeling of Places (Dejan Pangercic)
    • build an interface to Mechanical Turk for annotation of objects
    • build interface for creation of ontologies of objects
    • propose abstraction layer classify scenes using various ML classifiers (WUP, Binary Trees, SVM, CRF)
    • Research Areas: webGL, machine learning, knowledge representation
    • Tools: Linux, ROS
  7. Barcode-based Object Recognition (Dejan Pangercic)
    • build a barcode-based object recognition system (in collaboration with Barcoo)
    • get grasp of the object detection and pick-up system
    • find/enable web camera ROS driver with autofocus support
    • barcode finding algorithm
    • create an object ontology
    • Research Areas: drivers, image recognition, control theory, knowledge representation
    • Tools: Linux, ROS, OpenCV, Android
  8. Memory modeling for object perception (Nico Blodow)
    • higher-level approach to remembering object detection & locations
    • key problem: remember where objects are, what they look like
    • loosely modeled after human memory (short term / long term memory)
    • per-location map integration of memory, per-object collection of visual sensory inputs
    • Research Areas: cognitive robotics, artificial intelligence
    • Tools: Linux, ROS
  9. Detecting small objects in cluttered scenes (Nico Blodow)
    • build a system that can detect a small set of objects in possibly cluttered scenes using 2D and 3D image processing
    • combination of depth / surface cues and RGB cues (e.g. color, edges)
    • in this project, objects will be BRIO child toys (wooden train track pieces)
    • Research areas: object detection
    • Tools: Linux, ROS, OpenCV, PCL (possibly CUDA)
  10. Multi-view CAD-model matching to Kinect data (Zoltan-Csaba Marton)
    • start off with am RGB-Depth frame sequence from the Kinect camera
    • detect objects (and their approximate pose) – using an existing method
    • find the exact pose of the object by model fitting (use existing method)
    • extend system to account for the information about empty regions of space
    • extension to keep/improve multiple hypotheses over time (see particle filter)
    • Tools: Linux, ROS, PCL (possibly OpenCV)
  11. 3D object tracking system (after detection) (Nico Blodow)
    • Problem: When grasping, robot might move the object before establishing a stable grasp
    • 2D tracking methods often give poor 3D poses
    • combination of conventional tracking with depth reprojection + occlusion handling
    • Research areas: object tracking, computer vision
    • Tools: Linux, ROS, OpenCV, PCL
  12. Context-aware Object Recognition Architecture (Zoltan-Csaba Marton)
    • no general architecture for knowledge-enabled perception system
    • several sub-parts are implemented already in open-source projects
    • related architectures are implemented in related field (e.g.Wattson - the question answering program that won Jeopardy)
    • survey existing architectures (UIAM, AI-Goggles, Proctor, MOPED,BLORT) and highlight their advantages
    • based on the lessons learned, create a prototype for an object recognition system that makes decisions about an object's types given heterogeneous information
    • Research Areas: knowledge representation, cognitive vision, identity resolution
    • Tools: Linux, ROS, partially Java, and a bit of creativity :P
  13. Real-Time Point Cloud Annotation in RViz (David Gossow)
    • Build a system that allows a user to select 3D regions of interest in a point cloud
    • Use SLAM to accurately track the position of the selected region while the robot is moving
    • Collect training data (Point clouds/labeled depth+RGB images) from that
    • Train object recognition algorithms on training set
    • Evaluate algorithms on annotated point cloud test set
    • Research areas: computer vision, 3D processing, user interfaces
    • Tools: Linux, PCL, OpenCV, ROS, RViz+Interactive Markers, C++
  14. A Combined Geometry & Texture Based Interest Point Detector (David Gossow)
    • Extend an existing algorithm to jointly detect salient texture and geometry features (blobs)
    • Combine with (existing) feature descriptor, e.g. BRIEF
    • Evaluate for tasks such as object recognition, RGBD-SLAM
    • Research areas: computer vision, 3D processing
    • Tools: Linux, OpenCV, PCL, C++
  15. Fast & Reliable Face Detection using a Kinect Camera (David Gossow)
    • Extend the current standard face detection algorithm (Viola/Jones) in OpenCV to exploit depth information
    • Use existing intensity-based classifier from OpenCV
    • Write a detector that, instead of constructing a scale pyramid, directly infers the classification window size at each image position from camera parameters and depth information
    • Make use of integral images to efficiently compute mean filters of variable scale
    • Create/use existing annotated test set & evaluate classification performance & speed against image-only approach
    • Research areas: computer vision, face detection, machine learning
    • Tools: Linux, OpenCV, C++
  16. Detecting and Picking-up Cups and Plates in the Dishwasher (Thomas Rühr)
    • Research areas: computer vision, 3D processing, PR2
    • Tools: Linux, PCL, ROS, C++
teaching/ws2011/240913002.txt · Last modified: 2013/01/17 13:58 (external edit)