GPU Accelerated Parallel Occupancy Voxel Based ICP for Position Tracking

Adrian Ratter, Claude Sammut
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Australasian Conference on Robotics and Automation, 2013

   title={GPU Accelerated Parallel Occupancy Voxel Based ICP for Position Tracking},

   author={Ratter, Adrian and Sammut, Claude},



Download Download (PDF)   View View   Source Source   



Tracking the position of a robot in an unknown environment is an important problem in robotics. Iterative closest point algorithms using range data are commonly used for position tracking, but can be computationally intensive. We describe a highly parallel occupancy grid iterative closest point position tracking algorithm designed for use on a GPU, that uses an Extended Kalman Filter to estimate motion between scans to increase the convergence rate. By exploiting the hardware structure of GPUs to rapidly find corresponding points and by using an occupancy map structure that can be safely modified in parallel with little synchronisation, our algorithm can run substantially faster than CPU based occupancy grid position tracking. We reduce the runtime from an average of 33ms to just 3ms on a commodity GPU, allowing our algorithm to use more dense and more frequent data, resulting in improved accuracy. Additionally, our solution can use a much larger occupancy grid without significantly impacting runtime.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

229 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1424 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: