#include <fast_lio/States.h> not present about fast_lio HOT 7 CLOSED

Hello I would like to ask you another question, we are in the process of buying an Avia sensor to experiment with your FAST_LIO package, I managed to install your github on a virtual machine with ubuntu 18.04 LTS and also on jetson TX2 Nvidia which is a system recommended by DJI for livox systems, the github is all compiled. Do you think the system also works on tx2? I would like to avoid buying 2c manifold too which costs a lot and is not available here in Italy. What do you advise me to do, have you done any tests on a computer with jetson TX2? My team and I would like to put your system on a dji drone to carry out tests on hollow mining sites in very large spaces even 400mt to verify the accuracy of the clouds, then we will compare your system with a TOPCON laser scanner to analyze the differences. We would be interested in integrating precise GNSS positioning to your system to georeference and orient the cloud topographically, within 2cm of precision. Are you developing something to merge IMU and PointClaud data with Rinex data? There are very cheap UBLOX modules such as ublox FP9 that can manage dual band L1 L2 gnss signals which, if doubled, can create the baseline for orientation in space topographically. It would be interesting to combine your Fast_Lio package with a LOG system with satellite rinex data that aligns the cloud with centimeter precision which could then in post processing reach below the centimeter and color the clouds with a Sony a6000 camera. Hello from Italy.

Thanks for your interests in our works.

Here are something you may be interested in.

1. We will launch Fast-LIO 2.0 in coming March. The Fast-LIO 2.0 will support the real-time implementation in embedded systems including TX2, Raspberry Pi 4 and other ARM-based platforms. But current version of Fast-LIO cannot guarantee the real-time performance in TX2, especially for the Large-scale space. If you are still interested in implementing Fast-lio, please wait for the Fast-LIO 2.0.

2. FAST-LIO is focused on the fusion of IMU and LiDAR, not considering other sensor like GPS or Vision. But our MaRS Lab will propose some research projects that fusion other sensors, in a few months. So stay tuned!

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You can try to repeat "CMake" several times.

from fast_lio.

sorry but the fast_lio / states library where is it? it shouldn't be in the include directory of your package, otherwise how can it compile? Where should it be?

from fast_lio.

sorry but the fast_lio / states library where is it? it shouldn't be in the include directory of your package, otherwise how can it compile? Where should it be?

thanks for your suggestion，the states.h is automatically generated by the state.msg file in /msg folder. This is a common ros technique

from fast_lio.

Hello I would like to ask you another question, we are in the process of buying an Avia sensor to experiment with your FAST_LIO package, I managed to install your github on a virtual machine with ubuntu 18.04 LTS and also on jetson TX2 Nvidia which is a system recommended by DJI for livox systems, the github is all compiled. Do you think the system also works on tx2? I would like to avoid buying 2c manifold too which costs a lot and is not available here in Italy. What do you advise me to do, have you done any tests on a computer with jetson TX2? My team and I would like to put your system on a dji drone to carry out tests on hollow mining sites in very large spaces even 400mt to verify the accuracy of the clouds, then we will compare your system with a TOPCON laser scanner to analyze the differences. We would be interested in integrating precise GNSS positioning to your system to georeference and orient the cloud topographically, within 2cm of precision. Are you developing something to merge IMU and PointClaud data with Rinex data? There are very cheap UBLOX modules such as ublox FP9 that can manage dual band L1 L2 gnss signals which, if doubled, can create the baseline for orientation in space topographically. It would be interesting to combine your Fast_Lio package with a LOG system with satellite rinex data that aligns the cloud with centimeter precision which could then in post processing reach below the centimeter and color the clouds with a Sony a6000 camera. Hello from Italy.

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@XW-HKU is FAST_LIO 2.0 the same as r2live?

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@XW-HKU is FAST_LIO 2.0 the same as r2live?

r2live use fast-lio2 as lidar-inertial front-end.

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