FastSLAM | Python simulation of FastSLAM

by nwang57 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | FastSLAM Summary

FastSLAM is a Python library typically used in Simulation, Pygame applications. FastSLAM has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Python simulation of FastSLAM.

Support

Quality

Security

License

Reuse

Support

FastSLAM has a low active ecosystem.

It has 45 star(s) with 16 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FastSLAM is current.

Quality

FastSLAM has 0 bugs and 0 code smells.

Security

FastSLAM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FastSLAM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FastSLAM does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

FastSLAM releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

FastSLAM saves you 180 person hours of effort in developing the same functionality from scratch.

It has 445 lines of code, 55 functions and 8 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed FastSLAM and discovered the below as its top functions. This is intended to give you an instant insight into FastSLAM implemented functionality, and help decide if they suit your requirements.

Update the robot pose
Pre - computes the data association for the given observation distribution
Computes the Jacobian for the given landmark
Multiply a multivariate normal distribution
Update the landmark distribution
Run the simulation
Resample the particles
Move the robot
Check if the given coordinates are within the bounds
Updates the weights for an observation
Finds the data association between an observation
Compute the Jacobian
Updates the landmark

Get all kandi verified functions for this library.

FastSLAM Key Features

No Key Features are available at this moment for FastSLAM.

FastSLAM Examples and Code Snippets

No Code Snippets are available at this moment for FastSLAM.

Community Discussions

Trending Discussions on FastSLAM

Is there a fast Numpy algorithm for mapping a Polar grid into a Cartesian grid?

QUESTION

Is there a fast Numpy algorithm for mapping a Polar grid into a Cartesian grid?

Asked 2019-Feb-01 at 08:54

I have a grid containing some data in polar coordinates, simulating data obtained from a LIDAR for the SLAM problem. Each row in the grid represents the angle, and each column represents a distance. The values contained in the grid store a weighted probability of the occupancy map for a Cartesian world.

After converting to Cartesian Coordinates, I obtain something like this:

This mapping is intended to work in a FastSLAM application, with at least 10 particles. The performance I am obtaining isn't good enough for a reliable application.

I have tried with nested loops, using the scipy.ndimage.geometric_transform library and accessing directly the grid with pre-computed coordinates.

In those examples, I am working with a 800x800 grid.

Nested loops: aprox 300ms

...

ANSWER

Answered 2019-Feb-01 at 08:54

I came across a piece of code that seems to behave x10 times faster (8ms):

Source https://stackoverflow.com/questions/54447379

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FastSLAM

You can download it from GitHub.
You can use FastSLAM like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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