Awesome

BreezySLAM

<a href="https://github.com/simondlevy/BreezySLAM">This repository</a> contains everything you need to start working with <a href="http://en.wikipedia.org/wiki/Lidar">Lidar</a> -based <a href="http://en.wikipedia.org/wiki/Simultaneous_localization_and_mapping">SLAM</a> in Python. (There is also support for Matlab, C++, and Java; however, because of the popularity of Python for this kind of work, I am no longer updating the code for those languages.) BreezySLAM works with Python 3 on Linux and Mac OS X, and with C++ on Linux and Windows. By using Python C extensions, we were able to get the Python and Matlab versions to run as fast as C++. For maximum efficiency on 32-bit platforms, we use Streaming SIMD extensions (Intel) and NEON (ARMv7) in the compute-intensive part of the code.

</p><p> BreezySLAM was inspired by the <a href="http://home.wlu.edu/%7Elambertk/#Software">Breezy</a> approach to Graphical User Interfaces developed by my colleague <a href="http://home.wlu.edu/%7Elambertk/">Ken Lambert</a>: an object-oriented Application Programming Interface that is simple enough for beginners to use, but that is efficient enough to scale-up to real world problems; for example, the mapping of an entire floor of a house, shown in the image above-right, made by a BreezySLAM <a href="https://www.linkedin.com/pulse/slam-your-robot-drone-python-150-lidar-chris-fotache">user</a>.

As shown in the following code fragment, the basic API is extremely simple: a constructor that accepts Lidar parameters and the size of the map (pixels) and mapping area (meters); a method for updating with the current scan; a method that returns the current robot position; and a method for retrieving the current map as a byte array.

<pre> from breezyslam.algorithms import RMHC_SLAM lidar = MyLidarModel() mapbytes = bytearray(800*800) slam = <b>RMHC_SLAM</b>(lidar, 800, 35) while True: scan = readLidar() slam.update(scan) x, y, theta = slam.<b>getpos</b>(scan) slam.getmap(mapbytes) </pre>

If odometry is available, it can also be passed into the update method.

</p><h3>Installing for Python</h3> <p> The BreezySLAM installation uses the popular <a href="http://docs.python.org/2/distutils/introduction.html">distutils</a> approach to installing Python packages, so all you should have to do is download and unzip the file, cd to <tt><b>BreezySLAM/python</b></tt>, and do <pre> sudo python3 setup.py install </pre>

For a quick demo, you can then cd to <tt><b>BreezySLAM/examples</b></tt> and do

<pre> make pytest </pre>

This will generate and display a PGM file showing the map and robot trajctory for the Lidar scan and odometry data in the log file <tt><b>exp2.dat</b></tt>. If you have the <a href="http://www.pythonware.com/products/pil/">Python Imaging Library</a> installed, you can also try the <b><tt>log2png.py</tt></b> script to generate a a PNG file instead.

If you have installed PyRoboViz, you can see a “live” animation by doing

<pre> make movie </pre>

You can turn off odometry by setting the <b><tt>USE_ODOMETRY</tt></b> parameter at the top of the Makefile to 0 (zero). You can turn off the particle-filter (Monte Carlo position estimation) by commenting-out <b><tt>RANDOM_SEED</tt></b> parameter.

<p>

To see what other features are available, do

<pre> pydoc3 breezyslam </pre>

By using the component classes <b>Map</b>, <b>Scan</b>, and <b>Position</b> and the <b>distanceScanToMap()</b> method, you can develop new algorithms and particle filters of your own.

<p><h3>Testing with the Hokuyo URG04LX</h3>

If you're running on Linux, you can install the <a href="http://home.wlu.edu/~levys/software/breezylidar/">BreezyLidar</a> package, the OpenCV Python package, and try the <b>urgslam.py</b> example in the examples folder.

<p><h3>Testing with the GetSurreal XV Lidar</h3>

BreezySLAM includes Python support for the inexpensive <a href="https://www.getsurreal.com/product/xv-lidar-sensor-mount-package">XV Lidar</a> from GetSurreal. To try it out, you'll also need the <a href="https://github.com/simondlevy/xvlidar">xvlidar</a> Python package. Once you've installed both packages, you can run the <b>xvslam.py</b> example in the <b>BreezySLAM/examples</b> folder.

<p><h3>Testing with the SLAMTEC RPLidar A1</h3>

BreezySLAM also includes partial Python support for the inexpensive <a href="http://www.slamtec.com/en/lidar/a1">RPLidar A1</a> from SLAMTECH. To try it out, you'll also need the <a href="https://github.com/SkoltechRobotics/rplidar">rplidar</a> Python package. Once you've installed that package, you can run the <b>rpslam.py</b> example in the <b>BreezySLAM/examples</b> folder.

Unfortunately, I and several users have had trouble using BreezySLAM with the RPLidar A1. The issues are inconsistent enough that I cannot provide support for this lidar unit if you run into trouble using it with BreezySLAM.

</p><h3>Installing for Matlab</h3> <p> 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 need, including pre-compiled binaries for all three operating systems. To try it out in Matlab, add this directory to your path, then change to the <b>examples</b> directory and do <pre> >> logdemo('exp2', 1) </pre>

If you modify the source code or want to build the binary for a different OS, you can change to the <b>matlab</b> directory and do

For making the binary on Windows I found <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.

<h3>Installing for C++</h3>

Just cd to <tt><b>BreezySLAM/cpp</b></tt>, and do

<pre> sudo make install </pre>

This will put the <tt><b>libbreezyslam</b></tt> shareable library in your <tt><b>/usr/local/lib</b></tt> directory. If you keep your shared libraries elsewhere, just change the <tt><b>LIBDIR</b></tt> variable at the top of the Makefile. You may also need to add the following line to your <b>~/.bashrc</b> file:

<pre> export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib </pre> <p>

For a quick demo, you can then cd to <tt><b>BreezySLAM/examples</b></tt> and do

<pre> make cpptest </pre> <p>

Again, you'll need to change the <tt><b>LIBDIR</b></tt> variable at the top of the Makefile in this directory as well, if you don't use <tt><b>/usr/local/lib</b></tt>.

</p><p> <h3>Installing for Java</h3>

In <tt><b>BreezySLAM/java/edu/wlu/cs/levy/breezyslam/algorithms</b></tt> and <tt><b>BreezySLAM/java/edu/wlu/cs/levy/breezyslam/components</b></tt>, edit the <tt>JDKINC</tt> variable in the Makefile to reflect where you installed the JDK. Then run <b>make</b> in these directories.

<p>

For a quick demo, you can then cd to <tt><b>BreezySLAM/examples</b></tt> and do

<pre> make javatest </pre> <h3>Notes on Windows installation</h3>

Because of the <a href="http://stackoverflow.com/questions/2817869/error-unable-to-find-vcvarsall-bat">difficulties</a> that I and others have had installing Python extensions on Windows, I am no longer supporting the Python version of this package on Windows. If you want to try it yourself, <a href="https://docs.python.org/2/extending/windows.html">here</a> are some instructions.

<p> To build and use the C++ library on Windows, I used MinGW. Whatever C++ compiler you use, you'll have to add the location of the <tt><b>.dll</b></tt> file to your <tt><b>PATH</b></tt> environment variable in the Advanced Systems Settings. <h3>Adding new particle filters</h3>

Because it is built on top of the CoreSLAM (<a href="https://openslam.org/tinyslam.html">tinySLAM</a>) code base, BreezySLAM provides a clean separation between the map-building and particle-filtering (Monte Carlo position estimation) components of SLAM. To add a new particle filter, you can subclass <a href="doc/breezyslam.algorithms.html#CoreSLAM">breezyslam.algorithms.CoreSLAM</a> or <a href="doc/breezyslam.algorithms.html#SinglePositionSLAM">breezyslam.algorithms.SinglePositionSLAM</a> classes, implementing the relevant methods.

<h3>Copyright, licensing, and questions</h3>

Copyright and licensing information (Gnu <a href="https://www.gnu.org/licenses/lgpl.html">LGPL</a>) can be found in the header of each source file.

<h3>Personnel</h3>

Suraj Bajracharya, Simon D. Levy, Matt Lubas, Alfredo Rwagaju

<h3>Acknowledgments</h3>

This work was supported in part by a Commonwealth Research Commercialization Fund grant from the Center for Innovative Technology (CRCF #MF14F-011-MS). We thank Michael Searing of Olin College for his help in debugging and testing this package.