Supplementary Material:
Privacy Preserving Visual SLAM
Mikiya Shibuya∗ 1,2 , Shinya Sumikura∗ 1 , and Ken Sakurada∗ 111

National Institute of Advanced Industrial Science and Technology (AIST)
2
Tokyo Institute of Technology{mikiya-shibuya,sumikura.shinya,k.sakurada}@aist.go.jp
shibuya@m.titech.ac.jp

Overview

This document is to provide a supplementary description of the main paper, especially concerning the quantitative and qualitative evaluation of our experimental
results in Sections 4.4 and 4.5. Additionally, visual materials such as trajectory
figures and maps illustrate the results for comparative analysis among the tested
datasets or applied methods.

2Dataset Details

Tables 1 and 2 show the details of the CARLA and KITTI datasets for quantitative evaluation in the main paper (Sec. 4.4), respectively. Table 3 shows thedetails of the Campus dataset (Scene A, B) and the Desk dataset for qualitativeevaluation (Sec. 4.5). Figures 1 and 2 show sample images of the CARLA andCampus datasets, respectively.
Table 1. CARLA dataset for quantitative evaluation∗Sequence pair ##01
#02 − 12

Image size
PerspectiveFisheye640 × 360
N/A1280 × 720
1280 × 720

Equirectangular
N/A2160 × 1080

#01
1m07s / 1m04s

Length (Sequence 1 / 2)#02#03
2m20s / 1m16s
2m08s / 1m28s#04
1m40s / 1m49s#05
2m11s / 1m34s#06
2m47s / 1m34s#072m20s / 2m00s#08
2m21s / 2m04s#09
2m26s / 1m58s#102m45s / 3m46s#112m56s / 2m53s#122m23s / 2m17s

The authors assert equal contribution and joint first authorship.

2M. Shibuya, S. Sumikura, K. Sakurada
Table 2. KITTI dataset for quantitative evaluation
Sequence pair
#00
#05

Image size
1246 × 376
1226 × 370

Length (Sequence 1 / 2)
1m15s / 6m18s
1m20s / 3m16s

Table 3. Campus dataset (Scene A, B) and Desk dataset for qualitative evaluation
Dataset
Scene A
Scene B
Desk

Dataset
Scene A
Scene B
Desk(a) Perspective

Image size
Fisheye1920 × 1080
1920 × 1080
N/A

Equirectangular1920 × 960
1920 × 960
N/A

Length (Sequence 1 / 2)
Perspective
Fisheye
5m13s / 5m55s
5m29s / 5m42s
3m23s / 2m44s
2m23s / 2m19s
3m14s / 3m13s
N/A

Equirectangular
4m47s / 5m00s
1m48s / 1m51s
N/A

Perspective
1920 × 1080
1920 × 1080
960 × 540(b) Fisheye(c) EquirectangularFig. 1. Sample images of the CARLA dataset. The three images in each row show thesame scene with diﬀerent projection models (perspective, fisheye, equirectangular).

(a) Perspective(b) Fisheye(c) EquirectangularFig. 2. Sample images of the Campus dataset (top: Scene A, bottom: Scene B).

Privacy Preserving Visual SLAM

33

Additional Results

Figure 3 shows the trajectories of the CARLA dataset for evaluation of thetracking time in the main paper. Figures 4 and 5 show the trajectories of theCARLA and KITTI datasets for evaluation of the localization accuracy. Figure
6 shows the trajectories of the CARLA dataset for evaluation of the eﬀectivenessof the global bundle adjustments.
Figure 7 shows an example of the tracking and mapping behavior of LCVSLAM for the privacy protection on the Desk dataset: (a) scene for prebuilt
map where personal objects exist (Sequence 1), (b) scene for LC-VSLAM where
the personal objects have been removed (Sequence 2), (c) a point cloud reconstructed from the video of the Sequence 1 with a standard point-based VisualSLAM, (d) a line cloud converted from the point cloud, and (e) mixed line andpoint clouds after tracking by LC-VSLAM. The 3D points of the concealed privacy objects are not restored near the corresponding 3D lines.
Figures 8 and 9 show all the results of the Campus dataset for the threeprojection models (perspective, fisheye and equirectangular). The blue lines andthe black points show the prebuilt line cloud and the reconstructed point cloudby LC-VSLAM, respectively. The green and red frames show the keyframes. Theorder from the first (left) to the last (right) columns represents examples of thereconstruction process.

6040y [m]

20020
4080

60

40

200 20x [m]406080(a) Sequence pair #01 of the CARLA dataset (Perspective projection model)
LC-VSLAM

p6LGround truth

Prebuilt mapFig. 3. Trajectories on the CARLA dataset for evaluation of the tracking time: estimated by LC-VSLAM (red), estimated by p6L (violet), ground truth (black), andprebuilt map (green).4M. Shibuya, S. Sumikura, K. Sakurada

1007550y [m]250255075100100 75

50250 25x [m]5075 100(a) Sequence pair #02 of the CARLA dataset (Perspective projection model)

1007550y [m]250255075
10075

50250 25x [m]5075100(b) Sequence pair #03 of the CARLA dataset (Perspective projection model)
LC-VSLAM

Point-based VSLAM

Ground truth

Prebuilt map

Fig. 4. Trajectories of the CARLA dataset (Sequences #02 and #03). For the evaluation of the localization accuracy: estimated by LC-VSLAM (red), estimated by astandard Visual SLAM with a point-cloud prebuilt map (blue), ground truth (black),
and prebuilt map (green).

Privacy Preserving Visual SLAM

57550y [m]250255075
100150100500x [m]50100y [m](c) Sequence pair #04 of the CARLA dataset (Fisheye projection model)

1007550250255075
100150100500x [m]50100150(d) Sequence pair #05 of the CARLA dataset (Perspective projection model)
LC-VSLAM

Point-based VSLAM

Ground truthPrebuilt mapFig. 4. Trajectories of the CARLA dataset (Sequences #04 and #05). For the evaluation of the localization accuracy: estimated by LC-VSLAM (red), estimated by astandard Visual SLAM with a point-cloud prebuilt map (blue), ground truth (black),and prebuilt map (green).

6M. Shibuya, S. Sumikura, K. Sakurada

100y [m]50050
100
150 100

50050x [m]100 150 200(e) Sequence pair #06 of the CARLA dataset (Equirectangular projection model)

150

100y [m]

50050

100

100500x [m]50100(f) Sequence pair #07 of the CARLA dataset (Perspective projection model)
LC-VSLAM

Point-based VSLAM

Ground truth

Prebuilt mapFig. 4. Trajectories of the CARLA dataset (Sequences #06 and #07). For the evaluation of the localization accuracy: estimated by LC-VSLAM (red), estimated by astandard Visual SLAM with a point-cloud prebuilt map (blue), ground truth (black),
and prebuilt map (green).

Privacy Preserving Visual SLAM

710050y [m]

050

100

150

200

150

100500x [m]50100150(g) Sequence pair #08 of the CARLA dataset (Equirectangular projection model)

200150100y [m]5005010015020050050100 150x [m]200250(h) Sequence pair #09 of the CARLA dataset (Fisheye projection model)
LC-VSLAM

Point-based VSLAM

Ground truth

Prebuilt mapFig. 4. Trajectories of the CARLA dataset (Sequences #08 and #09). For the evaluation of the localization accuracy: estimated by LC-VSLAM (red), estimated by astandard Visual SLAM with a point-cloud prebuilt map (blue), ground truth (black),
and prebuilt map (green).

8

M. Shibuya, S. Sumikura, K. Sakurada(a) Sequence pair #00 of the KITTI dataset(b) Sequence pair #05 of the KITTI dataset
LC-VSLAM

Point-based VSLAM

Ground truthPrebuilt mapFig. 5. Trajectories of the KITTI dataset (Sequences #00 and #05). For the evaluation of the localization accuracy: estimated by LC-VSLAM (red), estimated by astandard Visual SLAM with a point-cloud prebuilt map (blue), ground truth (black),
and prebuilt map (green).

Privacy Preserving Visual SLAM

9150
10050y [m]050100150200250200 150 100 500 50 100 150 200x [m](a) Sequence pair #10 of the CARLA dataset (Fisheye projection model)

200150y [m]10050050100150200 150 100 500 50 100 150 200x [m](b) Sequence pair #11 of the CARLA dataset (Fisheye projection model)
LC-VSLAM

Point-based VSLAM

Ground truthPrebuilt map

Fig. 6. Trajectories of the CARLA dataset (Sequences #10 and #11). For evaluationof the eﬀectiveness of the global bundle adjustments : estimated by LC-VSLAM (red),estimated by a standard Visual SLAM with a point-cloud prebuilt map (blue), groundtruth (black), and prebuilt map (blue).

10M. Shibuya, S. Sumikura, K. Sakurada

200150100y [m]50050100150200150 100 500 50 100 150x [m](c) Sequence pair #12 of the CARLA dataset (Fisheye projection model)
LC-VSLAM

Point-based VSLAM

Ground truth

Prebuilt map

Fig. 6. Trajectories of the CARLA dataset (Sequence #12). For evaluation of the eﬀectiveness of the global bundle adjustments : estimated by LC-VSLAM (red), estimatedby a standard Visual SLAM with a point-cloud prebuilt map (blue), ground truth(black), and prebuilt map (blue).

Privacy Preserving Visual SLAM

11BCAPrivacy objects(a) Video 1(b) Video 2(c) Point cloud(d) Line cloudBCA(e) Mixed line and point clouds

Fig. 7. Tracking and mapping behavior of LC-VSLAM for privacy protection on theDesk dataset: (a) scene for prebuilt map where personal objects exist (Video 1), (b)scene for LC-VSLAM where the personal objects have been removed (Video 2), (c) apoint cloud reconstructed from Video 1 with a standard point-based Visual SLAM, (d)a line cloud converted from the point cloud, and (e) mixed line and point clouds after
tracking by LC-VSLAM. It should be noted here that the 3D points of the concealed
privacy objects are not restored near the corresponding 3D lines.

12M. Shibuya, S. Sumikura, K. Sakurada

Perspective

Start

2nd floor
Current frame

1st floor
Current frame

Localized here

Loop connection

Drift is corrected

Slightly drifted

FisheyeStart

2nd floor

1st floor
Current frame

Localized here

Current frame
Loop connection

Slightly drifted

Equirectangular

Start

Localized here

2nd floor

1st floor

Current frame
Loop connection

Current frame

Slightly drifted(a) Relocalization

Drift is corrected(b) Before loop closure

Drift is corrected(c) After loop closure

Fig. 8. Behavior of relocalization, tracking, local and global mapping for each projection model on the Scene A of the Campus dataset: (a) relocalization, (b) drifted and(c) corrected trajectories before and after loop closure.

Privacy Preserving Visual SLAM

Current frame

Current frame

Expanded area
Slightly drifted

Loop connection
Drift is corrected

Current frame

Current frame

Expanded area
Slightly drifted

Loop connection
Drift is corrected

PerspectiveStart13Localized here

FisheyeStart

Localized hereCurrent frame

Current frame

Equirectangular

Start

Localized here(a) Relocalization

Expanded area
Slightly drifted(b) Before loop closure

Loop connection
Drift is corrected(c) After loop closure

Fig. 9. Behavior of relocalization, tracking, local and global mapping for each projection model on the Scene B of the Campus dataset: (a) relocalization, (b) drifted and(c) corrected trajectories before and after loop closure.