SLAM14讲-ch12 回环检测实践记录

安装BoW库
训练字典需要安装BoW库(DBoW3)https://github.com/rmsalinas/DBoW3
注:cmake工程,老方法编译安装即可,
记住sudo make install

编译cmake工程
mkdir build
cmake ..
make -j8
SLAM14讲-ch12 回环检测实践记录运行此程序,将看到如下字典信息输出:
SLAM14讲-ch12 回环检测实践记录分支数量k为10,深度L为5,单词书为4970,没有达到最大容量;Weighting表示权重,Scoring表示得分。

相似度计算

./build/loop_closure data

程序演示了两种对比:图像之间的对比,以及图像与数据库之间的对比。

hf@hf:~/code/slambook-master/ch12$ ./build/loop_closure data
reading database
reading images... 
detecting ORB features ... 
[ INFO:0] Initialize OpenCL runtime...
comparing images with images 
image 0 vs image 0 : 1
image 0 vs image 1 : 0.0238961
image 0 vs image 2 : 0.0198893
image 0 vs image 3 : 0.0339413
image 0 vs image 4 : 0.0160477
image 0 vs image 5 : 0.0300543
image 0 vs image 6 : 0.0222911
image 0 vs image 7 : 0.0197256
image 0 vs image 8 : 0.0228385
image 0 vs image 9 : 0.0583173

image 1 vs image 1 : 1
image 1 vs image 2 : 0.0422068
image 1 vs image 3 : 0.0282324
image 1 vs image 4 : 0.0225322
image 1 vs image 5 : 0.025146
image 1 vs image 6 : 0.0161122
image 1 vs image 7 : 0.0166048
image 1 vs image 8 : 0.0350047
image 1 vs image 9 : 0.0326933

image 2 vs image 2 : 1
image 2 vs image 3 : 0.0361374
image 2 vs image 4 : 0.0330401
image 2 vs image 5 : 0.0189786
image 2 vs image 6 : 0.0182909
image 2 vs image 7 : 0.0137075
image 2 vs image 8 : 0.0270761
image 2 vs image 9 : 0.0219518

image 3 vs image 3 : 1
image 3 vs image 4 : 0.0304209
image 3 vs image 5 : 0.03611
image 3 vs image 6 : 0.0205856
image 3 vs image 7 : 0.0208058
image 3 vs image 8 : 0.0312382
image 3 vs image 9 : 0.0329747

image 4 vs image 4 : 1
image 4 vs image 5 : 0.0498645
image 4 vs image 6 : 0.0345081
image 4 vs image 7 : 0.0227451
image 4 vs image 8 : 0.0208472
image 4 vs image 9 : 0.0266803

image 5 vs image 5 : 1
image 5 vs image 6 : 0.0198106
image 5 vs image 7 : 0.0162269
image 5 vs image 8 : 0.0259153
image 5 vs image 9 : 0.0220667

image 6 vs image 6 : 1
image 6 vs image 7 : 0.0212959
image 6 vs image 8 : 0.0188494
image 6 vs image 9 : 0.0208606

image 7 vs image 7 : 1
image 7 vs image 8 : 0.012798
image 7 vs image 9 : 0.0205016

image 8 vs image 8 : 1
image 8 vs image 9 : 0.0228186

image 9 vs image 9 : 1

comparing images with database 
database info: Database: Entries = 10, Using direct index = no. Vocabulary: k = 10, L = 5, Weighting = tf-idf, Scoring = L1-norm, Number of words = 4970
searching for image 0 returns 4 results:
<EntryId: 0, Score: 1>
<EntryId: 9, Score: 0.0583173>
<EntryId: 3, Score: 0.0339413>
<EntryId: 5, Score: 0.0300543>

searching for image 1 returns 4 results:
<EntryId: 1, Score: 1>
<EntryId: 2, Score: 0.0422068>
<EntryId: 8, Score: 0.0350047>
<EntryId: 9, Score: 0.0326933>

searching for image 2 returns 4 results:
<EntryId: 2, Score: 1>
<EntryId: 1, Score: 0.0422068>
<EntryId: 3, Score: 0.0361374>
<EntryId: 4, Score: 0.0330401>

searching for image 3 returns 4 results:
<EntryId: 3, Score: 1>
<EntryId: 2, Score: 0.0361374>
<EntryId: 5, Score: 0.03611>
<EntryId: 0, Score: 0.0339413>

searching for image 4 returns 4 results:
<EntryId: 4, Score: 1>
<EntryId: 5, Score: 0.0498645>
<EntryId: 6, Score: 0.0345081>
<EntryId: 2, Score: 0.0330401>

searching for image 5 returns 4 results:
<EntryId: 5, Score: 1>
<EntryId: 4, Score: 0.0498645>
<EntryId: 3, Score: 0.03611>
<EntryId: 0, Score: 0.0300543>

searching for image 6 returns 4 results:
<EntryId: 6, Score: 1>
<EntryId: 4, Score: 0.0345081>
<EntryId: 0, Score: 0.0222911>
<EntryId: 7, Score: 0.0212959>

searching for image 7 returns 4 results:
<EntryId: 7, Score: 1>
<EntryId: 4, Score: 0.0227451>
<EntryId: 6, Score: 0.0212959>
<EntryId: 3, Score: 0.0208058>

searching for image 8 returns 4 results:
<EntryId: 8, Score: 1>
<EntryId: 1, Score: 0.0350047>
<EntryId: 3, Score: 0.0312382>
<EntryId: 2, Score: 0.0270761>

searching for image 9 returns 4 results:
<EntryId: 9, Score: 1>
<EntryId: 0, Score: 0.0583173>
<EntryId: 3, Score: 0.0329747>
<EntryId: 1, Score: 0.0326933>

done.

for image 9 returns 4 results:
<EntryId: 9, Score: 1>
<EntryId: 0, Score: 0.0583173>
<EntryId: 3, Score: 0.0329747>
<EntryId: 1, Score: 0.0326933>

done.


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