Pose Estimation for a Mobile Device

Progress

Jan 24, 2013

• Add the point prior information into structure and pose estimation. Now the filtering result is kept in the optimization so the reconstruction result uses more measurements with slightly increasing the computation cost.

Jan 21, 2013

• Setup the android environment on Win7. Able to run sample programs on mobile devices.

 Environment
 - Android SDK with eclispe bundled for windows
 - Android NDK r8 for windows
 - OpenCV 2.4.3 for android. No need to recompile the library.

 Test device:
 - ASUS TF101
 - HTC EVO 3D

 Test program:
 - acceleration reader.
 - basic camera reader.

Jan 17, 2013

• Started project.

To Do List in a week

PC side:

• Remove features with inconsistent measurements.
• Add motion model of the camera into pose estimation cost function.
• Redesign the mechanism of adding features at each key frame. (We should only add features that make the density of the features are equally spread.)
• Identify the situation of the motion-blur. Keyframe should not be added at that time.

Mobile side:

• Perform feature detection on the mobile device and record its performance.
• Setup the connection between a mobile devise and the PC.

Goal

PC side:

• Refine the feature matching to consider the density of the features at a frame.

• Incorporate the motion model of the camera into optimization.

• Make the program to be multi-threaded.

Mobile side:

• Setup basic data capturing program

• Test on data transfer between mobile device and PC.

• Transfer part of preprocessing program to the mobile device.