Scene Simulation for RGB-D Camera Localization

Efficient Scene Simulation for Robust Monte Carlo Localization using an RGB-D Camera

We present Kinect Monte Carlo Localization
(KMCL), a new method for localization in three
dimensional indoor environments using RGB-D cameras, such
as the Microsoft Kinect. The approach makes use of a low
fidelity a priori 3-D model of the area of operation composed
of large planar segments, such as walls and ceilings, which are
assumed to remain static. Using this map as input, the KMCL
algorithm employs feature-based visual odometry as the particle
propagation mechanism and utilizes the 3-D map and the
underlying sensor image formation model to efficiently generate
simulated RGB-D camera views at the location of particle poses,
using a graphical processing unit (GPU). The generated 3D
views of the scene are then used to evaluate the likelihood of
the particle poses. This GPU implementation provides a factor
of ten speedup over a pure distance-based method, yet provides
comparable accuracy. Experimental results are presented for
five different configurations, including: (1) a robotic wheelchair,
(2) a sensor mounted on a person, (3) an Ascending Technologies
quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21.
The results demonstrate that the system can perform robust
localization with 3D information for motions as fast as 1.5
meters per second. The approach is designed to be applicable
not just for robotics but other applications such as wearable
computing.

Category:

Science & Technology

Tags:

• robotics
• robots
• localization
• mapping
• locate
• kinect
• rgb-d
• rgbd
• range
• image
• pcl
• pr2