Loading presentation...
Prezi is an interactive zooming presentation

Present Remotely

Send the link below via email or IM

Copy

Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.

DeleteCancel

Make your likes visible on Facebook?

Connect your Facebook account to Prezi and let your likes appear on your timeline.
You can change this under Settings & Account at any time.

No, thanks

Optimizing 3D Environment Reconstruction

No description
by

Yan Li

on 26 January 2015

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of Optimizing 3D Environment Reconstruction

Optimizing 3D Environment Reconstruction
Yan (Asta) Li
Kang Zhang, Dr. Xin Li

Introduction
A technique used to map unknown environment while identifying the pose (position and orientation) of the sensor
Implement and optimize simultaneous localization and mapping (SLAM); visualize accurate, dense 3D maps of unknown environments in real time
Goal:
SLAM:
Hardware:
Software:
Microsoft Kinect: infrared (IR) depth sensor and color (RGB) camera
Infrared Emitter
IR Depth Sensor
Color Sensor
C++ compiled in MS Visual Studio;
Mobile Robot Programming Toolkit (MRPT) framework and OpenCV libraries
Approach
Mobile Robot Programming Toolkit (MRPT)
~ open-source
~ simple, minimal structure
~ familiar C++ source code
Framework
Optimized Algorithm
Scan unknown environment
Detect and track features using SIFT
Compute descriptors and correspondences
Register point clouds and estimate pose
Visualize global mapping
Scale-Invariant Feature Transform (SIFT): robust feature detection and tracking algorithm, invariant to scale, rotation, viewpoint, illumination, and image distortion.
Conclusion
Limitations:
Results
Although the environment mapping is sparse, real-time and interactive frame rates during reconstruction are achieved with adequate accuracy.
~ Applications for unmanned aerial vehicles (UAVs)
~ Useful in GPS-denied environments
~ Assist with first-responders, police work, search and rescue
~ Real-time processing speed is a primary concern
Outlook:
~ Lack of graphics card stresses accuracy/time trade-offs
~ Hardware limitations: ineffective in direct sunlight, restricted maximum environment size
~ Accumulated position drift error (loop closure)

Full transcript