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A graph-based multi-sensor fusion framework. It can be used to fuse various relative or absolute measurments with IMU readings in real-time.

License: BSD 3-Clause "New" or "Revised" License

CMake 2.60% C++ 97.40%
factor-graph gnss imu lidar localization sensor-fusion state-estimation

graph_msf's Issues

second example

I want to ask when the second example code will be released. Your work is so good that I can't wait to see it.

Out of Range exeception while optimizing graph: invalid key

Hi,

running example with the 2_driving_away_filtered.bag data, the ROS node crashes after a short while to an error "Out of Range exeception while optimizing graph: invalid key".

I've tried to increase the smootherLag parameter (default value: 1.5) in config/graph_config.yaml, but it does not seem to fix the issue completely.

I get a same type of error "Out of Range exeception while optimizing graph: Requested variable "x35180" is not in this VectorValues. This happens if the measurement delay is larger than the graph smootherLag..."

I thought to ask if you would have more insight on how to overtcome this problem.

what is the reason to empty the globalFactorsBufferPtr

when reading the source code, I found that there are always empty global factors in the "updateActiveGraphAndGetState" function. As for the action, I think the non-linear graph contains only very few factors such as the latest IMU factor or Gnss factor so the isam2 optimizer only consists of a few constraints. What is the reason to empty the buffer?

When will the new version be released?

Hi! In other issure, I see you reply that there is a new version coming soon, I am very interested in it, I want to ask when the new version will be released, or can I get a trial access? Thank you so much!

Compslam

There is a launch file in graph_msf_ros for compslam_ros. But, I couldn't find your slam in the repo. I want to use your slam i.e., compslam. Is it available? If yes, where can I find it?

Waiting for the code

Hi, I really like the framework that you proposed.
And I am still waiting for the open-source code to do some researches.
Hope your can release it asap.
Appreciate your work again!

Regarding fusing a stereo inertial sensor for performing Visual SLAM

Hello @nubertj ! Thank you for this excellent work. I was a part of the ETH Robotics Summer school and I learnt a lot on using this code to map and localise.
I am currently working on my thesis on a similar topic at the moment and I wanted to ask if this Graph Msf can be used or modified to fuse stereo inertial cameras as well? Is it mandatory to have a LIDAR to use this SLAM method? I remember during the Summer school, since we tested a lot outdoors, the tracking camera gave problems and we disabled it.

My use case for my thesis is to perform indoors, is it possible that I can modify and add an implementation to fuse stereo inertial cameras?

Looking forward to your reply,
Aishwarya

Releasing the code

Just came across this on youtube, I was hoping you could release the code as promised on the repo that it will be released in may 2022

Pose and IMU fusion example?

Hi, and thank you for making this code available.

I need to add IMU data into existing pose data (xyz / xyzw) from a SLAM system.

The goal is to smooth and predict data when tracking is lost.

Is this code suitable for that purpose? Is there an example that might help me get started?

Thanks!

suitable for indoor scene?

Hello author,
Thanks for sharing the work! I roughly read the paper, if we deploy this dual graph into indoor scene for localization, is it suitable? I think the fallback graph of the paper is consider for gnss drop. In indoor environment, there is no gps, main graph always is active.

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