Cleanup
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README.md
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README.md
@@ -5,19 +5,19 @@ BreezySLAM
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<p><p><p>
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<p><p><p>
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<b>Simple, efficient, open-source package for Simultaneous Localization and Mapping in Python and C++</b>
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<a href="https://github.com/simondlevy/BreezySLAM">This repository</a> contains everything you need to
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<a href="https://github.com/simondlevy/BreezySLAM">This repository</a> contains everything you need to
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start working with
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start working with
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<a href="http://en.wikipedia.org/wiki/Lidar">Lidar</a>
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<a href="http://en.wikipedia.org/wiki/Lidar">Lidar</a>
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-based
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-based
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<a href="http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping">SLAM</a>
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<a href="http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping">SLAM</a>
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in Python or C++. There is also support for Matlab and Java, though I am no longer maintaining that
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in Python. (There is also support for Matlab, C++, and Java; however, because of the popularity of
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code.) BreezySLAM works with Python 2 and 3 on Linux and
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Python for this kind of work, I am no longer updating that code.)
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Mac OS X, and with C++ on Linux and Windows. By using Python C extensions, we
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BreezySLAM works with Python 2 and 3 on Linux and Mac OS X, and
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were able to get the Python version to run as fast as C++. For maximum effiency
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with C++ on Linux and Windows.
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on 32-bit platforms, we use Streaming SIMD extensions (Intel) and NEON (ARMv7)
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By using Python C extensions, we were able to get the Python and Matlab versions to run
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in the compute-intensive part of the code.
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as fast as C++. For maximum effiency on 32-bit platforms, we use Streaming
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SIMD extensions (Intel) and NEON (ARMv7) in the compute-intensive part
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of the code.
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</p><p>
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</p><p>
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BreezySLAM was inspired by the <a href="http://home.wlu.edu/%7Elambertk/#Software">Breezy</a>
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BreezySLAM was inspired by the <a href="http://home.wlu.edu/%7Elambertk/#Software">Breezy</a>
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approach to Graphical User Interfaces developed by my colleague
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approach to Graphical User Interfaces developed by my colleague
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@@ -120,6 +120,27 @@ To try it out, you'll also need the <a href="https://github.com/simondlevy/xvlid
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Python package. Once you've installed
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Python package. Once you've installed
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both packages, you can run the <b>xvslam.py</b> example in the <b>BreezySLAM/examples</b> folder.
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both packages, you can run the <b>xvslam.py</b> example in the <b>BreezySLAM/examples</b> folder.
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</p><h3>Installing for Matlab</h3>
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<p>
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I have run BreezySLAM in Matlab on 64-bit Windows, Linux, and Mac OS X. The <b>matlab</b> directory contains all the code you
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need, including pre-compiled binaries for all three operating systems. To try it out in Matlab, add this directory to your
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path, then change to the <b>examples</b> directory and do
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<pre>
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>> logdemo('exp2', 1)
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</pre>
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If you modify the source code or want to build the binary for a different OS, you can change to the <b>matlab</b>
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directory and do
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<pre>
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>> make
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</pre>
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For making the binary on Windows I found
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<a href="http://www.mathworks.com/matlabcentral/answers/95039-why-does-the-sdk-7-1-installation-fail-with-an-installation-failed-message-on-my-windows-system">these instructions</a> very helpful when I ran into trouble.
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<h3>Installing for C++</h3>
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<h3>Installing for C++</h3>
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Just cd to <tt><b>BreezySLAM/cpp</b></tt>, and do
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Just cd to <tt><b>BreezySLAM/cpp</b></tt>, and do
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@@ -147,6 +168,21 @@ the Makefile in this directory as well, if you don't use <tt><b>/usr/local/lib</
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</p><p>
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</p><p>
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<h3>Installing for Java</h3>
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In <tt><b>BreezySLAM/java/edu/wlu/cs/levy/breezyslam/algorithms</b></tt> and
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<tt><b>BreezySLAM/java/edu/wlu/cs/levy/breezyslam/components</b></tt>,
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edit the <tt>JDKINC</tt> variable in the Makefile to reflect where you installed the JDK.
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Then run <b>make</b> in these directories.
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<p>
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For a quick demo, you can then cd to <tt><b>BreezySLAM/examples</b></tt> and do
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<pre>
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make javatest
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</pre>
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<h3>Notes on Windows installation</h3>
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<h3>Notes on Windows installation</h3>
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