Project Proposal

CIS 565 Final Project Pitch

GPU Accelerated RGB-D SLAM with Microsoft Kinect

Yedong Niu

03/11/2012

Background
The simultaneous localization and mapping (SLAM) problem asks if it is possible for a mobile robot to be placed at an unknown location in an unknown environment and for the robot to incrementally build a consistent map of this environment while simultaneously determining its location within this map[1]. While vision based SLAM is one of the most recent approaches in the SLAM community, RGB-D(epth) method with affordable Microsoft Kinect sensor is a typical implementation. The real-time application is a challenge as it deals with gigantic amount of point data with limited hardware resources on a mobile robot. GPU implementation may solve the problem above somehow.

Goal
My project aims to improve the performance of real-time 3D environment reconstruction[5] with Kinect by using CUDA. I will mainly focus on improving the efficiency of related computer vision algorithms including registration, feature extraction and matching(SIFT/SURF and RANSAC), and Iterative Closest Point (ICP) algorithm. My project will base on N. Engelhard’s paper[2] and apply GPU application on every appropriate step. The model takes 2 seconds per frame on Intel i&@2GHz[2], which is the baseline where I started. I may use some OpenCV[3] and PCL[4] GPU libraries if allowed.

The above 3D reconstruction is based on point clouds. An optional goal is to reconstruct the environment by geometry-based surfaces, which is more challenging but more rewarding in some applications such as virtual touch input[6]. As time is limited, I don’t know whether I could reach this goal finally.

6D SLAM with RGB-D Data from Kinect [5]

KinectFusion[6]

Reference
[1] Hugh Durrant-Whyte, Tim Bailey, Simultaneous Localization and Mapping: Part I, 2006
[2] N. Engelhard, F. Endres and etal, Real-time 3D visual SLAM with a hand-held RGB-D camera, 2011
[3] OpenCV GPU documentation 2.3, http://opencv.itseez.com/modules/gpu/doc/gpu.html, 2012
[4] PCL documentation, http://pointclouds.org/documentation/, 2012
[5] N. Engelhard, http://www.youtube.com/watch?v=XejNctt2Fcs, 6D SLAM with RGB-D Data from Kinect
[6] Shahram Izadi and etal, KinectFusion: Realtime 3D Reconstruction and Interaction Using a Moving Depth Camera, pp563, 2011