├── README.md
├── aslam_base
├── link
├── param.yml
├── slam_server.cpp
└── stereo.yml


/README.md:
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 1 | # A\*SLAM Software
 2 | 
 3 | For more information see
 4 | [https://astar.ai](https://astar.ai).
 5 | 
 6 | The following steps have been tested and passed on Ubuntu **16.04.5**.
 7 | 
 8 | ### 1. Hardware Requirements
 9 | 
10 | It is recommended to run A\*SLAM in a computer with 4-core, 8-thread CPU (i7-8550U or better) and 8G RAM.
11 | 
12 | ### 2. OpenCV Installation
13 | 
14 | Follow the steps in [CaliCam@GitHub](https://github.com/astar-ai/calicam).
15 | 
16 | ### 3. Download Image Data
17 | 
18 | Make sure the uncompressed files are under the directory  /home/USER_NAME/data/aslam_indoor
19 | 
20 | 	cd
21 | 	mkdir data
22 | 	cd data
23 | 	wget http://astar.support/dotai/aslam_indoor.tar.gz
24 | 	tar xvf aslam_indoor.tar.gz
25 | 
26 | ### 4. Download Program
27 | 
28 | 	cd
29 | 	git clone https://github.com/astar-ai/aslam.git
30 | 	cd aslam
31 | 	chmod 777 ./link
32 | 	./link
33 | 
34 | ### 5. Run
35 | 
36 | To run A\*SLAM program, you need to change the name of CaliCam stereo parameter file '##-YYMMDD-####.yml' to 'stereo.yml' first.
37 | 
38 | 	./aslam
39 | 
40 | ### 6. Operation
41 | 
42 | Plese refer to the **Operation** part of [**A\*SLAM Manual**](https://drive.google.com/open?id=1T_PiYxxNShu9rh6vtca9-kar4itFgyLR0am5_G2h0-s).
43 | 
44 | ### 7. SSML: Monocualr Localization
45 | 
46 | To run A\*SLAM program in the Monocular Localization mode, you need to change the name of CaliCam monocular parameter file '##-YYMMDD-####.yml' to 'mono.yml' first.
47 | 
48 | You also need to set the following parameters in the 'param.yml' file.
49 | 
50 | 	slam_mode: 0
51 | 	model: mono
52 | 
53 | 
54 | 


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/aslam_base:
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https://raw.githubusercontent.com/astar-ai/aslam/36c232e719d19ef95df1e4b3ec266969980d786a/aslam_base


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/link:
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1 | g++ -L/usr/local/lib -o "aslam" aslam_base -lGL -lGLU -lX11 -lpthread -lopencv_core -lopencv_highgui -lopencv_features2d -lopencv_flann -lopencv_videoio -lopencv_imgcodecs -lopencv_imgproc -lopencv_calib3d -lopencv_xfeatures2d
2 | 


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/param.yml:
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 1 | %YAML:1.0
 2 | feat_num:    1000
 3 | pcl_lvl:     1
 4 | pyr_levels:  3
 5 | port_pose:   8001
 6 | live:        0
 7 | foward_slam: 1
 8 | threed_slam: 1
 9 | slam_mode:   1
10 | network:     0
11 | track_gl:    1
12 | kf_dist:     0.3
13 | kf_angle:    10.
14 | data_path:   /data/aslam_indoor
15 | map_path:    ./map
16 | host:        /127.0.0.1
17 | model:       stereo
18 | img_size:    [ 640, 480 ]
19 | usb_port:    0
20 | 


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/slam_server.cpp:
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 1 | #include <unistd.h>
 2 | #include <iostream>
 3 | #include <thread>
 4 | #include <netdb.h>
 5 | #include <strings.h>
 6 | 
 7 | using namespace std;
 8 | 
 9 | int main(int argc, char** argv) {
10 |   while (1) {
11 |     int sockfd, new_sockfd;
12 |     socklen_t cli_len;
13 |     sockaddr_in serv_addr, cli_addr;
14 | 
15 |     sockfd = socket(AF_INET, SOCK_STREAM, 0);
16 |     if (sockfd < 0)
17 |       cout << "ERROR opening socket" << endl;
18 | 
19 |     bzero((char *) &serv_addr, sizeof(serv_addr));
20 |     serv_addr.sin_family = AF_INET;
21 |     serv_addr.sin_addr.s_addr = INADDR_ANY;
22 |     serv_addr.sin_port = htons(8001);
23 | 
24 |     if (bind(sockfd, (sockaddr *) &serv_addr, sizeof(serv_addr)) < 0)
25 |       cout << "ERROR on binding" << endl;
26 | 
27 |     listen(sockfd, 5);
28 | 
29 |     cli_len = sizeof(cli_addr);
30 |     new_sockfd = accept(sockfd, (sockaddr *) &cli_addr, &cli_len);
31 |     if (new_sockfd < 0)
32 |       cout << "ERROR on accept" << endl;
33 | 
34 |     cout << "Connected" << endl;
35 | 
36 |     struct SlamData {
37 |       int   status;
38 |       float inl;
39 |       float x, y, z;
40 |       float qx, qy, qz, qw;
41 |     };
42 | 
43 |     SlamData data;
44 | 
45 |     while (1) {
46 |       int n = read(new_sockfd, &data, sizeof(SlamData));
47 | 
48 |       if (n < 0)
49 |         cout << "ERROR reading from socket" << endl;
50 | 
51 |       if (n == 0)
52 |         continue;
53 | 
54 |       cout << data.status << " " << data.inl << " "
55 |            << data.x  << " " << data.y  << " " << data.z  << " "
56 |            << data.qx << " " << data.qy << " " << data.qz << " " << data.qw
57 |            << endl;
58 | 
59 |       usleep(100);
60 |     }
61 | 
62 |     close(new_sockfd);
63 |     close(sockfd);
64 |   }
65 | 
66 |   cout << "Why is here? " << endl;
67 | 
68 |   return 0;
69 | }
70 | 
71 | 


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/stereo.yml:
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 1 | %YAML:1.0
 2 | ---
 3 | cam_model: stereo
 4 | Kl: !!opencv-matrix
 5 |    rows: 3
 6 |    cols: 3
 7 |    dt: d
 8 |    data: [ 1.3754184409888023e+03, 1.5650637059075301e-01,
 9 |        6.3309166975009839e+02, 0., 1.3734421459279981e+03,
10 |        4.7400072552292659e+02, 0., 0., 1. ]
11 | Dl: !!opencv-matrix
12 |    rows: 1
13 |    cols: 4
14 |    dt: d
15 |    data: [ -8.4309901562773598e-02, 3.7385085922367500e-01,
16 |        -7.6386412374314339e-04, -1.0009405307866118e-03 ]
17 | xil: !!opencv-matrix
18 |    rows: 1
19 |    cols: 1
20 |    dt: d
21 |    data: [ 2.5176685329699322e+00 ]
22 | Rl: !!opencv-matrix
23 |    rows: 3
24 |    cols: 3
25 |    dt: d
26 |    data: [ 9.9999112446630034e-01, -4.2131893576937982e-03,
27 |        -4.9051368293944009e-06, 4.2131893577444842e-03,
28 |        9.9999112447833050e-01, 0., 4.9050932937461801e-06,
29 |        -2.0666270287885013e-08, 9.9999999998796985e-01 ]
30 | Kr: !!opencv-matrix
31 |    rows: 3
32 |    cols: 3
33 |    dt: d
34 |    data: [ 1.3735012179792418e+03, 1.1578919618060471e-01,
35 |        6.3754772405051881e+02, 0., 1.3716437710883588e+03,
36 |        4.6351537189058809e+02, 0., 0., 1. ]
37 | Dr: !!opencv-matrix
38 |    rows: 1
39 |    cols: 4
40 |    dt: d
41 |    data: [ -7.8979337567376601e-02, 4.4824386621556778e-01,
42 |        9.7390374455825135e-04, -1.3279403348263653e-03 ]
43 | xir: !!opencv-matrix
44 |    rows: 1
45 |    cols: 1
46 |    dt: d
47 |    data: [ 2.5202273471100547e+00 ]
48 | Rr: !!opencv-matrix
49 |    rows: 3
50 |    cols: 3
51 |    dt: d
52 |    data: [ 9.9995873999988394e-01, -4.0406701804792632e-03,
53 |        8.1358025011118128e-03, 4.0811603492106295e-03,
54 |        9.9997933952362550e-01, -4.9663523936529352e-03,
55 |        -8.1155670195336618e-03, 4.9993509965291436e-03,
56 |        9.9995457099888541e-01 ]
57 | T: !!opencv-matrix
58 |    rows: 3
59 |    cols: 1
60 |    dt: d
61 |    data: [ -1.2084041392890925e-01, 4.8831000547324224e-04,
62 |        -9.8569434300089945e-04 ]
63 | key_str: LUWSJBPFZI
64 | RMSE: 3.4327463710249528e-01
65 | cap_size: [ 2560, 960 ]
66 | 


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