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{
            
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  "author": "Raphael Voges",
2
  "author": "Raphael Voges",
            
3
  "author_email": "voges@rts.uni-hannover.de",
3
  "author_email": "voges@rts.uni-hannover.de",
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "doi": "10.25835/0026408",
5
  "doi": "10.25835/0026408",
            
6
  "doi_date_published": "2020-08-17",
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  "doi_date_published": "2020-08-17",
            
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  "doi_publisher": "LUIS",
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  "doi_publisher": "LUIS",
            
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  "doi_status": "true",
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  "doi_status": "true",
            
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  "domain": "https://data.uni-hannover.de",
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  "domain": "https://data.uni-hannover.de",
            
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  "groups": [],
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  "groups": [],
            
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  "have_copyright": "Yes",
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  "have_copyright": "Yes",
            
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  "id": "0bcea595-0786-44f6-a9e2-c26a779a004b",
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  "id": "0bcea595-0786-44f6-a9e2-c26a779a004b",
            
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  "isopen": false,
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  "isopen": false,
            
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  "license_id": "CC-BY-NC-3.0",
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  "license_id": "CC-BY-NC-3.0",
            
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  "license_title": "CC-BY-NC-3.0",
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  "license_title": "CC-BY-NC-3.0",
            
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  "maintainer": "Raphael Voges",
16
  "maintainer": "Raphael Voges",
            
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  "maintainer_email": "",
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  "maintainer_email": "",
            
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  "metadata_created": "2021-10-14T10:15:59.415164",
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  "metadata_created": "2021-10-14T10:15:59.415164",
            
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  "metadata_modified": "2023-01-12T13:14:16.307584",
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  "metadata_modified": "2023-08-04T08:46:33.326149",
            
20
  "name": "luh-i-c-sens-visual-inertial-lidar-dataset",
20
  "name": "luh-i-c-sens-visual-inertial-lidar-dataset",
            
21
  "notes": "The _i.c.sens Visual-Inertial-LiDAR Dataset_ is a data set 
21
  "notes": "The _i.c.sens Visual-Inertial-LiDAR Dataset_ is a data set 
            
22
for the evaluation of dead reckoning or SLAM approaches in the context 
22
for the evaluation of dead reckoning or SLAM approaches in the context 
            
23
of mobile robotics. It consists of street-level monocular RGB camera 
23
of mobile robotics. It consists of street-level monocular RGB camera 
            
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images, a front-facing 180\u00b0 point cloud, angular velocities, 
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images, a front-facing 180\u00b0 point cloud, angular velocities, 
            
25
accelerations and an accurate ground truth trajectory. In total, we 
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accelerations and an accurate ground truth trajectory. In total, we 
            
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provide around 77 GB of data resulting from a 15 minutes drive, which 
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provide around 77 GB of data resulting from a 15 minutes drive, which 
            
27
is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the 
27
is split into 8 rosbags of 2 minutes (10 GB) each. Besides, the 
            
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intrinsic camera parameters and the extrinsic transformations between 
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intrinsic camera parameters and the extrinsic transformations between 
            
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all sensor coordinate systems are given. Details on the data and its 
29
all sensor coordinate systems are given. Details on the data and its 
            
30
usage can be found in the provided [documentation 
30
usage can be found in the provided [documentation 
            
31
e3-b69e-eb6461abc57b/download/sensor_platform_small.jpg)\r\n\r\n*Image 
31
e3-b69e-eb6461abc57b/download/sensor_platform_small.jpg)\r\n\r\n*Image 
            
32
credit: S\u00f6ren Vogel*\r\n\r\nThe data set was acquired in the 
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credit: S\u00f6ren Vogel*\r\n\r\nThe data set was acquired in the 
            
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context of the measurement campaign described in 
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context of the measurement campaign described in 
            
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[Schoen2018](https://doi.org/10.3390/s18072400). Here, a vehicle, 
34
[Schoen2018](https://doi.org/10.3390/s18072400). Here, a vehicle, 
            
35
which can be seen below, was equipped with a self-developed sensor 
35
which can be seen below, was equipped with a self-developed sensor 
            
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platform and a commercially available Riegl VMX-250 Mobile Mapping 
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platform and a commercially available Riegl VMX-250 Mobile Mapping 
            
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System. This Mobile Mapping System consists of two laser scanners, a 
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System. This Mobile Mapping System consists of two laser scanners, a 
            
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camera system and a localization unit containing a highly accurate 
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camera system and a localization unit containing a highly accurate 
            
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GNSS/IMU 
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GNSS/IMU 
            
40
-8821-4c46-b411-7d63491963ed/download/vehicle_small.jpg)\r\n\r\n*Image 
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-8821-4c46-b411-7d63491963ed/download/vehicle_small.jpg)\r\n\r\n*Image 
            
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credit: S\u00f6ren Vogel*\r\n\r\nThe data acquisition took place in 
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credit: S\u00f6ren Vogel*\r\n\r\nThe data acquisition took place in 
            
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May 2019 during a sunny day in the Nordstadt of Hannover 
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May 2019 during a sunny day in the Nordstadt of Hannover 
            
43
([coordinates](https://goo.gl/maps/o81D5awanuGbUgUu9): 52.388598, 
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([coordinates](https://goo.gl/maps/o81D5awanuGbUgUu9): 52.388598, 
            
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9.716389). The route we took can be seen below. This route was 
44
9.716389). The route we took can be seen below. This route was 
            
45
completed three times in total, which amounts to a total driving time 
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completed three times in total, which amounts to a total driving time 
            
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of 15 
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of 15 
            
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9c1e-26964f552d6c/download/google_earth_overview_small.png)\r\n\r\nThe 
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9c1e-26964f552d6c/download/google_earth_overview_small.png)\r\n\r\nThe 
            
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self-developed sensor platform consists of several sensors. This 
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self-developed sensor platform consists of several sensors. This 
            
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dataset provides data from the following sensors:\r\n\r\n* Velodyne 
49
dataset provides data from the following sensors:\r\n\r\n* Velodyne 
            
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HDL-64 __LiDAR__\r\n* LORD MicroStrain 3DM-GQ4-45 GNSS aided 
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HDL-64 __LiDAR__\r\n* LORD MicroStrain 3DM-GQ4-45 GNSS aided 
            
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__IMU__\r\n* Pointgrey GS3-U3-23S6C-C __RGB camera__\r\n\r\nTo inspect 
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__IMU__\r\n* Pointgrey GS3-U3-23S6C-C __RGB camera__\r\n\r\nTo inspect 
            
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the data, first start a rosmaster and launch rviz using the provided 
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the data, first start a rosmaster and launch rviz using the provided 
            
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configuration file:\r\n\r\n    roscore & rosrun rviz rviz -d 
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configuration file:\r\n\r\n    roscore & rosrun rviz rviz -d 
            
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icsens_data.rviz\r\nAfterwards, start playing a rosbag with\r\n\r\n    
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icsens_data.rviz\r\nAfterwards, start playing a rosbag with\r\n\r\n    
            
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rosbag play icsens-visual-inertial-lidar-dataset-{number}.bag 
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rosbag play icsens-visual-inertial-lidar-dataset-{number}.bag 
            
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--clock\r\n\r\nBelow we provide some exemplary images and their 
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--clock\r\n\r\nBelow we provide some exemplary images and their 
            
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corresponding point 
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corresponding point 
            
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b432-711916cb204c/download/combined_examples_small.jpg)\r\n\r\nRelated 
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b432-711916cb204c/download/combined_examples_small.jpg)\r\n\r\nRelated 
            
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publications:\r\n---------------------------\r\n\r\n* R. Voges, C. S. 
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publications:\r\n---------------------------\r\n\r\n* R. Voges, C. S. 
            
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Wieghardt, and B. Wagner, \u201cFinding Timestamp Offsets for a 
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Wieghardt, and B. Wagner, \u201cFinding Timestamp Offsets for a 
            
61
Multi-Sensor System Using Sensor Observations,\u201d Photogrammetric 
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Multi-Sensor System Using Sensor Observations,\u201d Photogrammetric 
            
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Engineering & Remote Sensing, vol. 84, no. 6, pp. 357\u2013366, 
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Engineering & Remote Sensing, vol. 84, no. 6, pp. 357\u2013366, 
            
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2018.\r\n\r\n* R. Voges and B. Wagner, \u201cRGB-Laser Odometry Under 
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2018.\r\n\r\n* R. Voges and B. Wagner, \u201cRGB-Laser Odometry Under 
            
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Interval Uncertainty for Guaranteed Localization,\u201d in Book of 
64
Interval Uncertainty for Guaranteed Localization,\u201d in Book of 
            
65
Abstracts of the 11th Summer Workshop on Interval Methods (SWIM 2018), 
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Abstracts of the 11th Summer Workshop on Interval Methods (SWIM 2018), 
            
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Rostock, Germany, Jul. 2018.\r\n\r\n* R. Voges and B. Wagner, 
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Rostock, Germany, Jul. 2018.\r\n\r\n* R. Voges and B. Wagner, 
            
67
\u201cTimestamp Offset Calibration for an IMU-Camera System Under 
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\u201cTimestamp Offset Calibration for an IMU-Camera System Under 
            
68
Interval Uncertainty,\u201d in IEEE/RSJ International Conference on 
68
Interval Uncertainty,\u201d in IEEE/RSJ International Conference on 
            
69
Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 
69
Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 
            
70
2018.\r\n\r\n* R. Voges and B. Wagner, \u201cExtrinsic Calibration 
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2018.\r\n\r\n* R. Voges and B. Wagner, \u201cExtrinsic Calibration 
            
71
Between a 3D Laser Scanner and a Camera Under Interval 
71
Between a 3D Laser Scanner and a Camera Under Interval 
            
72
Uncertainty,\u201d in Book of Abstracts of the 12th Summer Workshop on 
72
Uncertainty,\u201d in Book of Abstracts of the 12th Summer Workshop on 
            
73
Interval Methods (SWIM 2019), Palaiseau, France, Jul. 2019.\r\n\r\n* 
73
Interval Methods (SWIM 2019), Palaiseau, France, Jul. 2019.\r\n\r\n* 
            
74
R. Voges, B. Wagner, and V. Kreinovich, \u201cEfficient Algorithms for 
74
R. Voges, B. Wagner, and V. Kreinovich, \u201cEfficient Algorithms for 
            
75
Synchronizing Localization Sensors Under Interval Uncertainty,\u201d 
75
Synchronizing Localization Sensors Under Interval Uncertainty,\u201d 
            
76
Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 
76
Reliable Computing (Interval Computations), vol. 27, no. 1, pp. 
            
77
1\u201311, 2020.\r\n\r\n* R. Voges, B. Wagner, and V. Kreinovich, 
77
1\u201311, 2020.\r\n\r\n* R. Voges, B. Wagner, and V. Kreinovich, 
            
78
\u201cOdometry under Interval Uncertainty: Towards Optimal Algorithms, 
78
\u201cOdometry under Interval Uncertainty: Towards Optimal Algorithms, 
            
79
with Potential Application to Self-Driving Cars and Mobile 
79
with Potential Application to Self-Driving Cars and Mobile 
            
80
Robots,\u201d Reliable Computing (Interval Computations), vol. 27, no. 
80
Robots,\u201d Reliable Computing (Interval Computations), vol. 27, no. 
            
81
1, pp. 12\u201320, 2020.\r\n\r\n* R. Voges and B. Wagner, 
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1, pp. 12\u201320, 2020.\r\n\r\n* R. Voges and B. Wagner, 
            
82
\u201cSet-Membership Extrinsic Calibration of a 3D LiDAR and a 
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\u201cSet-Membership Extrinsic Calibration of a 3D LiDAR and a 
            
83
Camera,\u201d in IEEE/RSJ International Conference on Intelligent 
83
Camera,\u201d in IEEE/RSJ International Conference on Intelligent 
            
84
Robots and Systems (IROS), Las Vegas, NV, USA, Oct. 2020, 
84
Robots and Systems (IROS), Las Vegas, NV, USA, Oct. 2020, 
            
85
accepted.\r\n\r\n* R. Voges, \u201cBounded-Error Visual-LiDAR Odometry 
85
accepted.\r\n\r\n* R. Voges, \u201cBounded-Error Visual-LiDAR Odometry 
            
86
on Mobile Robots Under Consideration of Spatiotemporal 
86
on Mobile Robots Under Consideration of Spatiotemporal 
            
87
Uncertainties,\u201d PhD thesis, Gottfried Wilhelm Leibniz 
87
Uncertainties,\u201d PhD thesis, Gottfried Wilhelm Leibniz 
            
88
Universita\u0308t, 2020.\r\n\r\n",
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Universita\u0308t, 2020.\r\n\r\n",
            
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    "description": "Graduiertenkolleg 2159 
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    "description": "Graduiertenkolleg 2159 
            
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\r\nhttps://www.icsens.uni-hannover.de/",
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\r\nhttps://www.icsens.uni-hannover.de/",
            
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  "repository_name": "Leibniz University Hannover",
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      "description": "Vehicle used for our measurement 
            
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campaign\r\n\r\n*Image credit: S\u00f6ren Vogel*",
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campaign\r\n\r\n*Image credit: S\u00f6ren Vogel*",
            
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      "description": "The route we took for our measurement 
            
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campaign.",
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campaign.",
            
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      "id": "e1752747-e6e6-432f-97c5-ede595798a66",
            
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      "name": "dead reckoning",
            
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      "id": "28ed8fde-a3cf-4984-b13d-3e72d0e5d08e",
            
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      "name": "ground truth",
            
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      "state": "active",
            
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    {
            
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      "display_name": "imu",
            
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      "id": "ce346173-1ad7-4e59-9b6b-0818ea322d6b",
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      "id": "ce346173-1ad7-4e59-9b6b-0818ea322d6b",
            
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      "name": "imu",
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      "name": "imu",
            
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      "state": "active",
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      "state": "active",
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "inertial",
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      "display_name": "inertial",
            
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      "id": "4c40c28f-1d2a-423a-9ab5-26ece4bac22c",
            
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      "name": "inertial",
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      "name": "inertial",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
            
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    {
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    {
            
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      "display_name": "laser scanner",
            
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      "id": "25cab6c5-55c1-4d71-bccd-7da1a3ed5c97",
            
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      "name": "laser scanner",
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      "name": "laser scanner",
            
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      "state": "active",
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      "state": "active",
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "lidar",
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      "display_name": "lidar",
            
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      "id": "99f87ef3-0dc1-490b-a5df-7b34300e4430",
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      "id": "99f87ef3-0dc1-490b-a5df-7b34300e4430",
            
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      "name": "lidar",
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      "name": "lidar",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "localization",
562
      "display_name": "localization",
            
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      "id": "5638703a-b0ac-43e8-9b67-5530f1d38480",
563
      "id": "5638703a-b0ac-43e8-9b67-5530f1d38480",
            
563
      "name": "localization",
564
      "name": "localization",
            
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      "state": "active",
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      "state": "active",
            
565
      "vocabulary_id": null
566
      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "odometry",
569
      "display_name": "odometry",
            
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      "id": "a7f816ed-9a08-4388-bc73-3f4410f10461",
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      "id": "a7f816ed-9a08-4388-bc73-3f4410f10461",
            
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      "name": "odometry",
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      "name": "odometry",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "robotics",
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      "display_name": "robotics",
            
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      "id": "67a95a0a-dadc-477b-914a-0d659a29d2b7",
577
      "id": "67a95a0a-dadc-477b-914a-0d659a29d2b7",
            
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      "name": "robotics",
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      "name": "robotics",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
581
    {
582
    {
            
582
      "display_name": "visual",
583
      "display_name": "visual",
            
583
      "id": "9e5f1aea-9842-4090-9a35-9a4682b713e5",
584
      "id": "9e5f1aea-9842-4090-9a35-9a4682b713e5",
            
584
      "name": "visual",
585
      "name": "visual",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    }
588
    }
            
588
  ],
589
  ],
            
589
  "terms_of_usage": "Yes",
590
  "terms_of_usage": "Yes",
            
590
  "title": "i.c.sens Visual-Inertial-LiDAR Dataset",
591
  "title": "i.c.sens Visual-Inertial-LiDAR Dataset",
            
591
  "type": "vdataset",
592
  "type": "vdataset",
            
592
  "url": 
593
  "url": 
            
593
/data.uni-hannover.de/dataset/i-c-sens-visual-inertial-lidar-dataset",
594
/data.uni-hannover.de/dataset/i-c-sens-visual-inertial-lidar-dataset",
            
594
  "version": ""
595
  "version": ""
            
595
}
596
}