fastslam | This program is a pure C implementation of FastSLAM | GPU library

by bushuhui C++ Version: Current License: No License

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kandi X-RAY | fastslam Summary

fastslam is a C++ library typically used in Hardware, GPU, Qt5 applications. fastslam has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

This program is a pure C++ implementation of FastSLAM 1 and 2. It also integrated a GUI interface which draw current states and observations. The source is based on yglee source code (and orignal FastSLAM (. We add Qt GUI and also fixed some bugs from yglee's implementation.

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fastslam has a low active ecosystem.

It has 76 star(s) with 43 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 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 no bugs reported.

Security

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

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.

Installation instructions are not available. Examples and code snippets are available.

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fastslam Key Features

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fastslam Examples and Code Snippets

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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.

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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