Comments (7)
Hey @Juuustin, we currently only tested SuMa++ with the KITTI Odometry dataset. You could find more data type information on their original website.
For testing your own dataset, the semantic inferring may not work very well since probably the sensor model and the environments where you obtain the laser data are quite different from those of KITTI. You may need to first train or finetune a new model to correctly infer the semantic information, which you could find more information in the rangenet++ project.
Hope this helps.
from semantic_suma.
Hey @Juuustin, we currently only tested SuMa++ with the KITTI Odometry dataset. You could find more data type information on their original website.
For testing your own dataset, the semantic inferring may not work very well since probably the sensor model and the environments where you obtain the laser data are quite different from those of KITTI. You may need to first train or finetune a new model to correctly infer the semantic information, which you could find more information in the rangenet++ project.
Hope this helps.
Thank you for your reply. I had a look at the KITTI Odometry dataset, there are at least 4 type of dataset:
Download odometry data set (grayscale, 22 GB)
Download odometry data set (color, 65 GB)
Download odometry data set (velodyne laser data, 80 GB)
Download odometry data set (calibration files, 1 MB)
I am wondering which is used in this algorithm, and is it possible to use laser date only?
Also, what type of input is needed? for example, do we need to transform it into ROS bag?
from semantic_suma.
SuMa++ is a Lidar-only SLAM method. you therefore need only Velodyne laser data to run SuMa++. If you want to later evaluate the odometry results, you need also to download the calibration files and ground truth poses.
from semantic_suma.
SuMa++ is a Lidar-only SLAM method. you therefore need only Velodyne laser data to run SuMa++. If you want to later evaluate the odometry results, you need also to download the calibration files and ground truth poses.
Sounds great, I will have a try, thank you very much!
from semantic_suma.
SuMa++ is a Lidar-only SLAM method. you therefore need only Velodyne laser data to run SuMa++. If you want to later evaluate the odometry results, you need also to download the calibration files and ground truth poses.
Sounds great, I will have a try, thank you very much!
Thank you for using our code. I will appreciate it a lot if you could star&fork this repo or cite the related paper if possible.
from semantic_suma.
SuMa++ is a Lidar-only SLAM method. you therefore need only Velodyne laser data to run SuMa++. If you want to later evaluate the odometry results, you need also to download the calibration files and ground truth poses.
Sounds great, I will have a try, thank you very much!
Thank you for using our code. I will appreciate it a lot if you could star&fork this repo or cite the related paper if possible.
No problem, I have stared it and I will cite it if it could run on my computer
from semantic_suma.
SuMa++ is a Lidar-only SLAM method. you therefore need only Velodyne laser data to run SuMa++. If you want to later evaluate the odometry results, you need also to download the calibration files and ground truth poses.
Sounds great, I will have a try, thank you very much!
Thank you for using our code. I will appreciate it a lot if you could star&fork this repo or cite the related paper if possible.
No problem, I have stared it and I will cite it if it could run on my computer
Thanks, if you have any problem please feel free to drop me a msg.
from semantic_suma.
Related Issues (20)
- Can not detect loop closure sometimes! HOT 2
- (error)
- What is the role of rosnode in the visualization window? HOT 2
- the question about the part of code !! HOT 1
- `SyntaxError: invalid syntax`. HOT 3
- Formula error(Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments formula 10) HOT 2
- different result on suma++ and rangenet++ HOT 6
- different result with Disks mode HOT 1
- Pls, how do I save the global map? HOT 3
- The problem when i catkin deps fetch HOT 2
- the real-time performance? HOT 1
- i can not save pose HOT 3
- savepose HOT 1
- save poses HOT 7
- There are some issues with dynamic object culling HOT 1
- error when ./visualizer HOT 1
- The result of semantic segmentation seems incorrect
- 对‘glow::glTranslate(float, float, float)’未定义的引用 HOT 7
- error ld return 1 existing status HOT 2
- error: returning a value from a constructor HOT 2
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