Project for MILD: An efficient loop closure detection libary based on binary features.

Related papers:

1. Multi-Index Hashing for Loop closure Detection. International Conference on Multimedia Expo, 2017. Best Student Paper Awards.

2. 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');