ICPCUDA

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Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher

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Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher

ICPCUDA

Super fast implementation of ICP in CUDA for compute capable devices 3.5 or higher. On an NVIDIA GeForce GTX TITAN X it runs at over 750Hz (using projective data assocation). Last tested with Ubuntu 18.04.2, CUDA 10.1 and NVIDIA drivers 418.39.

Requires CUDA, includes Pangolin, Eigen and Sophus third party submodules. I've built it to take in raw TUM RGB-D datasets to do frame-to-frame dense ICP as an example application.

Install;

bash

sudo apt-get install build-essential cmake libglew-dev libpng-dev
git clone https://github.com/mp3guy/ICPCUDA.git
cd ICPCUDA
git submodule update --init
cd third-party/Pangolin/
mkdir build
cd build/
cmake ../ -DEIGEN_INCLUDE_DIR=
make -j12
cd ../../../
mkdir build
cd build/
cmake ..
make -j12

The particular version of ICP implemented is the one introduced by KinectFusion. This means a three level coarse-to-fine registration pyramid, from 160x120 to 320x240 and finally 640x480 image sizes, with 4, 5 and 10 iterations per level respectively.

Run like;

bash

./ICP ~/Desktop/rgbd_dataset_freiburg1_desk/ -v

Where ~/Desktop/rgbd_dataset_freiburg1_desk/ contains the depth.txt file, for more information see here.

The main idea to getting the best performance is determining the best thread/block sizes to use. I have provided an exhaustive search function to do this, since it varies between GPUs. Simply pass the "-v" switch to the program to activate the search. The code will then first do a search for the best thread/block sizes and then run ICP and output something like this on an nVidia GeForce GTX TITAN X;

bash

GeForce GTX TITAN X
Searching for the best thread/block configuration for your GPU...
Best: 256 threads, 96 blocks (1.3306ms), 100%
ICP: 1.3236ms
ICP speed: 755Hz

The code will output one file; output.poses. You can evaluate it on the TUM benchmark by using their tools. I get something like this;

bash

python ~/stuff/Kinect_Logs/Freiburg/evaluate_ate.py ~/Desktop/rgbd_dataset_freiburg1_desk/groundtruth.txt output.poses 
0.144041

The difference in values comes down to the fact that each method uses a different reduction scheme and floating point operations are not associative.

Also, if you're using this code in academic work and it would be suitable to do so, please consider referencing some of my possibly relevant research in your literature review/related work section.