ElasticFusion | Real-time dense visual SLAM system | Robotics library

 by   mp3guy C++ Version: Current License: Non-SPDX

kandi X-RAY | ElasticFusion Summary

kandi X-RAY | ElasticFusion Summary

ElasticFusion is a C++ library typically used in Automation, Robotics applications. ElasticFusion has no bugs, it has no vulnerabilities and it has medium support. However ElasticFusion has a Non-SPDX License. You can download it from GitHub.

Real-time dense visual SLAM system capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera.

            kandi-support Support

              ElasticFusion has a medium active ecosystem.
              It has 1609 star(s) with 581 fork(s). There are 81 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 218 have been closed. On average issues are closed in 1428 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ElasticFusion is current.

            kandi-Quality Quality

              ElasticFusion has 0 bugs and 0 code smells.

            kandi-Security Security

              ElasticFusion has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              ElasticFusion code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              ElasticFusion has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              ElasticFusion releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.

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            ElasticFusion Key Features

            No Key Features are available at this moment for ElasticFusion.

            ElasticFusion Examples and Code Snippets

            No Code Snippets are available at this moment for ElasticFusion.

            Community Discussions


            Camera lens distortion in OpenGL
            Asked 2019-Oct-21 at 12:25

            I'm trying to simulate lens distortion effect for my SLAM project. A scanned color 3D point cloud is already given and loaded in OpenGL. What I'm trying to do is render 2D scene at a given pose and do some visual odometry between the real image from a fisheye camera and the rendered image. As the camera has severe lens distortion, it should be considered in the rendering stage too.

            The problem is that I have no idea where to put the lens distortion. Shaders?

            I've found some open codes that put the distortion in the geometry shader. But this one I guess the distortion model is different from the lens distortion model in Computer Vision community. In CV community, lens distortion usually occurs on the projected plane.

            This one is quite similar to my work but they didn't used distortion model.

            Anyone have a good idea?

            I just found another implementation. Their code implemented the distortion in both of fragment shader and geometry shader. But fragment shader version can be applied in my situation. Thus, I guess the following will work:



            Answered 2017-Jun-12 at 06:48

            Lens distortion usually turns straight lines into curves. When rasterizing lines and triangles using OpenGL, the primitives' edges however stay straight, no matter how you transform the vertices.

            If your models have fine enough tesselation, then incorporating the distortion into the vertex transformation is viable. It also works if you're rendering only points.

            However when your aim is general applicability you have to somehow deal with the straight edged primitives. One way is by using a geometry shader to further subdivide incoming models; or you can use a tesselation shader.

            Another method is rendering into a cubemap and then use a shader to create a lens equivalent for that. I'd actually recommend that for generating fisheye images.

            The distortion itself is usually represented by a polynomial of order 3 to 5, mapping undistorted angular distance from the optical center axis to the distorted angular distance.

            Source https://stackoverflow.com/questions/44489686


            ElasticFusion Slam algorithm running with pose graph on TUM RGB-D benchmark
            Asked 2018-Jul-20 at 08:22

            I downloaded Freiburg desk dataset from TUM RGB-D SLAM Dataset and Benchmark and converted it to '.klg' which is custom format of slam algorithm . I loaded this klg file to ElasticFusion and runned the SLAM algorithm. The 3d reconstruction output seems good enough while doing it.

            Now i want to build 3d reconstruction by already built trajectory information. I retrieved trajectory data from previous run from '.freibrug' and converted it to desired format by ElasticFusion. I just changed timestamp from seconds to microsenconds by multiplying it to 1000000. And split the variables using "," instead of " " space . I run the algorithm this time with "-p" flag and path information to trajectory file. Below is my running command.



            Answered 2017-Aug-03 at 14:04

            I took 3 scans: left-to-right, down-to-up and back-to-front. I observed that thought trajectory file seems correct , the building is going wrong. When I move the camera on x axis , on EF it moves in z axis and similar situation for the others. I tried to found transformation matrix manually. I applied this transformation to translation and rotation. It started to work afterwards.

            Source https://stackoverflow.com/questions/45411489


            Logging to klg file using kinect 1
            Asked 2017-Jun-20 at 15:07

            I am trying to run SLAM algorithm (ElasticFusion) using my custom .klg file. I tried the following 2 ways :

            The first way was about to build .klg file manually from separate depth and rgb image (.png) files and their time stamp informations . I tried conversion script on this Sequence 'freiburg1_desk' dataset and then run ElasticFusion . I get good result and point cloud. But when I tried to record environment on my own device with following the same steps, I did not get desired result or point cloud. The result which i am getting in live logging is much better . I guess it is because of the code that i am using for depth image visualization.



            Answered 2017-Jun-20 at 14:06


            Second way worked after re-installing OpenNI. Probably in previous runs Logger somehow was not able to find OpenNI for streaming the depth and rgb.

            Source https://stackoverflow.com/questions/44633676

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


            No vulnerabilities reported

            Install ElasticFusion

            Pangolin must be installed AFTER all the other libraries to make use of optional dependencies. Make sure you are running ElasticFusion on your nVidia GPU. In particular, if you have an Optimus GPU.
            If you use Prime, follow instructions here
            If you use Bumblebee, remember to run as optirun ./ElasticFusion


            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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