Official RTAB-Map Forum

RTAB MAP Need CUDA?

Classic

List

Threaded

2 messages Options

GGYU

RTAB MAP Need CUDA?

Hello I am ubuntu 22.04 users.

Does RTAB-Map vary depending on CUDA?

I wonder if the performance varies greatly depending on CUDA.

matlabbe

Re: RTAB MAP Need CUDA?

Administrator

Depending on the algorithm, you can get a big boost using CUDA. See related parameters with suffix "Gpu":

rtabmap --params | grep -e Gpu -e GPU
Param: FAST/Gpu = "false"                                  [GPU-FAST: Use GPU version of FAST. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.]
Param: FAST/GpuKeypointsRatio = "0.05"                     [Used with FAST GPU.]
Param: GFTT/Gpu = "false"                                  [GPU-GFTT: Use GPU version of GFTT. This option is enabled only if OpenCV>=3 is built with CUDA and GPUs are detected.]
Param: Kp/NNStrategy = "1"                                 [kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4]
Param: ORB/Gpu = "false"                                   [GPU-ORB: Use GPU version of ORB. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.]
Param: SIFT/Gpu = "false"                                  [CudaSift: Use GPU version of SIFT. This option is enabled only if RTAB-Map is built with CudaSift dependency and GPUs are detected.]
Param: SURF/GpuKeypointsRatio = "0.01"                     [Used with SURF GPU.]
Param: SURF/GpuVersion = "false"                           [GPU-SURF: Use GPU version of SURF. This option is enabled only if OpenCV is built with CUDA and GPUs are detected.]
Param: Stereo/Gpu = "false"                                [[Stereo/OpticalFlow=true] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA).]
Param: Vis/CorFlowGpu = "false"                            [[Vis/CorType=1] Enable GPU version of the optical flow approach (only available if OpenCV is built with CUDA).]
Param: Vis/CorNNType = "1"                                 [[Vis/CorType=0] kNNFlannNaive=0, kNNFlannKdTree=1, kNNFlannLSH=2, kNNBruteForce=3, kNNBruteForceGPU=4, BruteForceCrossCheck=5, SuperGlue=6, GMS=7. Used for features matching approach.]

In offline processing, the results should be fairly the same. Using GPU would be used if you need speed (e.g., higher VO frame rate) in online processing, which is particularly useful for robots running on Nvidia Jetson boards.