SLAMBench – Performance, accuracy and power efficiency benchmark for SLAM applications.

SLAMBench

Performance, accuracy and power efficiency benchmark for SLAM applications.

SLAMBench screenshot 0SLAMBench screenshot 1SLAMBench screenshot 2SLAMBench screenshot 3SLAMBench screenshot 4

This is the mobile edition of SLAMBench 1.1 using the ICL-NUIM dataset.

You can use this benchmark to evaluate the capability of your phone running augmented reality solutions based on SLAM algorithms (ie. KinectFusion).

SLAMBench : apt.cs.manchester.ac.uk/projects/PAMELA/tools/SLAMBench/
KinectFusion: msdn.microsoft.com/en-us/library/dn188670.aspx
ICL-NUIM: doc.ic.ac.uk/~ahanda/VaFRIC/iclnuim.html

SLAMBench Description
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Computer vision algorithms for 3D scene understanding have enormous potential impacts for power constrained robotics application contexts. SLAMBench presents a foundation for quantitative, comparable and validatable experimental research to investigate trade-offs for performance, accuracy and energy consumption of an application that produces a dense 3D model of an arbitrary scene using an RGB-D camera.

Dense approaches to the simultaneous localisation and mapping (SLAM) problem are computationally expensive compared to sparse feature-based methods, but have important advantages in providing robust localisation and a highly-detailed model of the environment. SLAMBench is a software framework that supports research in hardware accelerators and software tools by comparison of performance, energy-consumption, and accuracy of the generated 3D model in the context of a known ground truth.

Features of the Android version
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– This app runs KinectFusion on your Android mobile,
– It provides statistics about it’s performance, including speed, accuracy, and for the compatible devices, power efficiency and temperature.
– Your results will be anonymously sent to a remote server, and used to improve SLAMBench and SLAM algorithms.

Features of the Linux version
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SLAMBench provides implementations of kfusion using popular languages, currently CUDA, OpenCL, OpenMP and C++. Input sequences can be provided in a number of standard formats, including OpenNI, or directly from an OpenNI compatible camera. The tool allows various parameters to be easily adjusted to trade off accuracy against performance, or power. Accuracy can be measured using scripts provided in association with the ICL-NUIM dataset, which provide high quality synthetically generated sequences as ground truth references.

The structure of the code base allows for alternative kernels or algorithms to be plugged in with relative ease and, again, the effect on performance and accuracy to be easily analysed.

The Qt based interface allows real time visualisation of performance figures, including power on ODROID-XUE/3, as well as visualisation of the 3D model as it is constructed.

Publications
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If you use SLAMBench in scientific publications, we would appreciate citations to the following paper (arxiv.org/abs/1410.2167):

Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM.
L. Nardi, B. Bodin, M. Z. Zia, J. Mawer, A. Nisbet, P. H. J. Kelly, A. J. Davison, M. Luján, M. F. P. O’Boyle, G. Riley, N. Topham, and S. Furber. Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM. In IEEE Intl. Conf. on Robotics and Automation (ICRA), May 2015. arXiv:1410.2167.

See detail information and download apk file for your android phone: googleplaystoreapks.com/category/libraries-demo

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