Project for MILD: An efficient loop closure detection libary based on binary features.
Related papers:
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Multi-Index Hashing for Loop closure Detection. International Conference on Multimedia Expo, 2017. Best Student Paper Awards.
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Beyond SIFT Using Binary features in Loop Closure Detection. IROS 2017.
Ubuntu 14.04
cmake 3.2.0
OpenCV 3.1 http://xfloyd.net/blog/?p=987
eigen3
octave (optional, only used for evaluation)
$ mkdir build
$ cd build
$ cmake ..
$ make
./mild imagelist.txt settings.yaml
input:
imageList.txt: indicats the path of each input RGB image per line settings.yaml: indicats the parameters used in loop closure detection
output:
output/imagelist/lcd_shared_flag.bin: detected loop closure are set as 1. To be used in the run_scritp.m to check the accuracy of the detected loop closure. output/imagelist/lcd_shared_score_mild.bin: the image similarity calculated using MILD. output/imagelist/relocalization_time_per_frame.bin: lcd time of each frame.
evluation: (based on MATLAB/OCTAVE)
evaluation('build/output/imageList_NewCollege/lcd_shared_flag.bin','build/output/imageList_NewCollege/lcd_shared_probability.bin','data/truthNewCollege.mat');