UTIAS Long-Term Localization and Mapping Dataset

OverviewRelated WorkHardwareDescriptionToolsDatasets


Overview


The UTIAS In the Dark and Multiseason datasets contain 159,549 stereo image pairs collected at the University of Toronto Institute for Aerospace Studies. In both experiments, a closed loop path was manually driven to create a Teach run then the robot autonomously re-traversed the path at staggered time intervals creating Repeat runs. Stereo keyframes were captured approximately every 0.2 metres with all keyframes accurately localized back to the Teach run. Relative transforms for each stereo pair are provided in human-readable text files. A set of Python tools is provided to work with the data. As few or as many repeat runs as desired can be downloaded. Potential uses for these datasets include research in deep visual localization, deep visual odometry, all-weather localization, and experience-based localization. They may also be useful for work on dense stereo reconstruction, simultaneous localization and mapping, robust image mapping, or saliency detection. See Related Work for more past uses.


In the Dark

The UTIAS In the Dark dataset is a collection of 39 runs of a 260 metre path that were collected approximately once an hour over 30 hours. Headlights were used to continue traversal at night. This provides a full cycle of daily lighting conditions that may be used for comparison. Challenges include high sun glare as well as varying shadows occurring at dusk and dawn. The scenery is a mix of buildings, fencing, roads, and off-road vegetation. In this dataset, low-grade GPS data is provided as an extra for 23 of the 39 runs.

Overview of In the Dark path

Multiseason

The UTIAS Multiseason dataset is a collection of 136 runs of a separate 160 metre loop that were collected approximately once per day from January 31, 2017 to May 27, 2017. The runs cover a range of seasonal appearances from mixed snow cover over dead vegetation to fully grown grass and shrubs in warm weather. They also feature a range of illumination changes due to both the varied start time of runs and the changing sun position throughout the spring. The scenery is mainly natural vegetation with some man-made structures. Weather varies from snowfall to freezing rain to overcast skies to full sun. High wind on later runs causes changes in vegetation geometry providing a challenge to visual odometry and localization algorithms.


Overview of Multiseason path


Related Work

UTIAS In the Dark and Multiseason were collected using Visual Teach & Repeat (VT&R), a system to allow highly accurate autonomous traversal of a previously manually driven path using only a vision sensor. For more information on VT&R, see Furgale and Barfoot (2010). These datasets are unique in capturing stereo imagery of the same off-road loop many times in different conditions. Accurate ground-truth is provided by VT&R's Multi-Experience Localization (MEL). It uses bridging experiences to match images captured under lighting and weather conditions that are drastically different from the original Teach pass. See MacTavish et al. (2018) for more information.

These datasets are most similar to the Oxford RobotCar Dataset. However, ours feature off-road terrain and the accurate autonomous traversals of VT&R provide a higher degree of viewpoint invariance. UTIAS In the Dark also captures short-term illumination changes in a systematic way, unconfounded by long-term structural or seasonal change. They also share similarities with the Symphony Lake Dataset in capturing a variety of weather and seasonal changes through repeated surveys of a natural environment. The Symphony Lake Dataset does not capture stereo data, however, and the same route is only approximately followed each run. Like the New College Dataset, our dataset captures stereo imagery of the same loop of a mixed environment, though ours was collected over multiple runs providing a far greater amount of appearance change. We believe these datasets are a novel contribution to the robotics and computer vision communities and will be of use to many researchers.

The following publications have used these datasets in their work. If you use one or both of these datasets, please email ben.congram [at] robotics.utias.utoronto.ca to be added to the list.

[1]     Gridseth M and Barfoot T D. “DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network”. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Paris, France, 31 May - 4 June 2020.

[2]     Clement L E, Gridseth M, Tomasi J, and Kelly J. "Learning Matchable Image Transformations for Long-term Metric Visual Localization". IEEE Robotics and Automation Letters (RAL), 5(2):1492–1499, January 2020. doi: 10.1109/LRA.2020.2967659.

[3]     Gridseth M and Barfoot T D. “Towards Direct Localization for Visual Teach and Repeat”. In Proceedings of the 16th Conference on Computer and Robot Vision (CRV). Kingston, Canada, 29-31 May 2019. doi: 10.1109/CRV.2019.00021.

[4]     Zhang N, Warren M, and Barfoot T D. “Learning Place-and-Time-Dependent Binary Descriptors for Long-Term Visual Localization”. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Brisbane, Australia, 21-25 May 2018. doi: 10.1109/ICRA.2018.8460674.

[5]     MacTavish K A, Paton M, and Barfoot T D. “Selective Memory: Recalling Relevant Experience for Long-Term Visual Localization”. Journal of Field Robotics, 35(8):1265–1292, 2018. doi: 10.1002/rob.21838.

[6]     MacTavish K A, Paton M, and Barfoot T D. “Night Rider: Visual Odometry Using Headlights”. In Proceedings of the 14th Conference on Computer and Robot Vision (CRV), pages 314–320. Edmonton, Alberta, 16-19 May 2017. doi: DOI 10.1109/CRV.2017.48.

[7]     MacTavish K A, Paton M, and Barfoot T D. “Visual Triage: A Bag-of-Words Experience Selector for Long-Term Visual Route Following”. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2065–2072. Singapore, 29 May - 3 June 2017. doi: 10.1109/ICRA.2017.7989238.

[8]     Paton M, MacTavish K A, Warren M, and Barfoot T D. “Bridging the Appearance Gap: Multi-Experience Localization for Long-Term Visual Teach and Repeat”. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1918–1925. Daejeon, Korea, 9-14 October 2016. doi: 10.1109/IROS.2016.7759303.


Hardware

Both datasets were captured aboard a Clearpath Robotics Grizzly Robotic Utility Vehicle. A Point Grey Bumblebee XB3 stereo camera was rigidly mounted to a mast on the Grizzly. The XB3 provides a 0.24 metre baseline and 512 x 384 resolution. The full stereo stream was collected at 16 Hz but has been downsampled with stereo keyframes provided approximately every 0.2 metres travelled. On-board headlights were rigidly mounted below the stereo camera and used to illuminate scenery during the night-time traversals of the In the Dark dataset. A GPS unit logged low-grade GPS data for a portion of the In the Dark dataset runs (though was not used to assist the autonomous traversals).

The camera parameters are given here: camera_parameters.txt

The transformation matrix describing the rigid transform from the vehicle frame to the camera frame, T_camera_vehicle, is given here: transform_camera_vehicle.txt

The transformation matrix describing the rigid transform from the vehicle frame to the GPS frame, T_gps_vehicle, is given here: transform_gps_vehicle.txt

Grizzly Hardware Setup

Description of Data Products

Pose Graph

To work with a dataset, start by downloading the Teach run, run_000000, and as many repeat runs as desired. Keyframes in the Teach run have been localized to the following Teach run keyframe via visual odometry. All repeat run keyframes are localized back to the Teach run via visual localization. The relative transformation between any two vertices in the dataset can be found by chaining poses through the Teach run. Note: transforms between topologically near vertices in the pose graph can be considered very accurate, but transformations between distant vertices should not be relied upon. The figure below illustrates the pose graph form with dots representing vertices and arrows representing transformations. The first number in the vertex ID tuple indicates the run number while the second number indicates the pose within the run.

Example pose graph

Coordinate Frames

The figure to the right illustrates the coordinate frames applicable to these datasets.

All transformation data is given as relative transformations of the vehicle frame between vertices. The vehicle frame is approximately situated in the centre of the Grizzly at axle level. Images are captured in the camera frame. The camera frame is centered at the left stereo camera. GPS measurements are recorded as absolute measurements of the GPS frame with respect to an inertial frame. The 4x4 homogeneous transformation matrices describing the rigid transformations between the sensor and vehicle frames are provided in the Hardware section.

Coordinate frame diagram

Transformation matrices use the T_to_from convention. For example, to transform a point expressed in frame_a to its coordinates expressed in frame_b one would do the following:

p_b = T_ba * p_a

Here, p_jb_in_b is the coordinates of point j with respect to point b in frame_b, and p_ja_in_a is the coordinates of point j with respect to point a in frame_a.

C_ba is the rotation matrix from frame_a to frame_b and r_ab is the translation vector from to , expressed in .


File Structure

The datasets are split into run folders. run_000017 contains the 17th repeat run. In each run folder, there is an image folder with subdirectories for the left and right stereo images. Images are named after their corresponding vertices. For example, run_000004/images/left/000065.png corresponds to vertex (4,65). Also in the run folder is a timestamps_images.txt file and a transformation file — either transforms_temporal.txt for Teach runs, or transforms_spatial.txt for Repeat runs. GPS measurements, when available, are provided in a separate text file, gps.txt, and their timestamps in timestamps_gps.txt.


Interpreting Text Files
Transformation Files

The transformation data is contained in human-readable, comma-delimited text files. Each row provides the SE(3) transformation matrix between two vertices. The first two values represent the vertex ID of vertex A, the second two values the ID of vertex B, then the remaining 16 values provide the transformation matrix in row-major order.

Timestamp Files

The timestamp files are also human-readable and comma-delimited with each row representing the timestamp of one vertex. The first value is the vertex number and the second a Unix timestamp in nanoseconds. These may be converted to UTC via standard datetime library functions.

GPS Files

The GPS measurement files are human-readable and comma-delimited with each row representing the closest GPS measurement to a particular vertex. The run number and vertex number are listed first followed by latitude, longitude, and altitude. A file containing the Unix timestamps of these measurements is also included.


Helpful Tools

To simplify processing this data, Python tools have been provided in a GitHub repository. The scripts parse the text files to build a pose graph from the stored runs. The created graph can be used to calculate both the metric transformations and topological distance between particular vertices. It can also be queried to return other vertices within a particular radius of a given vertex.


Dataset

This section provides individual zip files for each run containing the stereo images and text files for the transformations, timestamps, and GPS measurements if available. A Python downloader script has also been provided to aid in downloading large portions of the datasets. A video preview is provided for each run to quickly see weather and lighting conditions before downloading. Note: some runs in the Multiseason dataset do not form a complete loop.

Skip to Multiseason

In the Dark

Run

Start Time

Description

Preview

GPS

Number of Frames

Download

000000

July 19 - 08:58 EDT

morning, sunny

Yes

1116

.zip
[905 MB]

000001

July 19 - 09:09 EDT

morning, sunny, small shadows

Yes

1195

.zip
[924 MB]

000002

July 19 - 09:46 EDT

morning, sunny, small shadows

Yes

1092

.zip
[837 MB]

000003

July 19 - 10:58 EDT

morning, sunny, small shadows

No

1122

.zip
[854 MB]

000004

July 19 - 12:29 EDT

mid-day, cloudy

No

981

.zip
[722 MB]

000005

July 19 - 16:48 EDT

afternoon, sunny, small shadows

Yes

1086

.zip
[818 MB]

000006

July 19 - 18:36 EDT

evening, sunny, medium shadows, large sun glare

Yes

1602

.zip
[1117 MB]

000007

July 19 - 19:31 EDT

evening, sunny, long shadows, large sun glare

No

2220

.zip
[1393 MB]

000008

July 19 - 20:01 EDT

evening, low sun, large sun glare

No

2652

.zip
[1485 MB]

000009

July 19 - 20:17 EDT

evening, low sun, large sun glare

Yes

2505

.zip
[1407 MB]

000010

July 19 - 20:30 EDT

evening, low sun, small sun glare

Yes

958

.zip
[651 MB]

000011

July 19 - 20:44 EDT

dusk, low sun

Yes

886

.zip
[607 MB]

000012

July 19 - 21:00 EDT

dusk, low sun

Yes

862

.zip
[590 MB]

000013

July 19 - 21:12 EDT

dusk, little natural light

Yes

2226

.zip
[1613 MB]

000014

July 19 - 21:26 EDT

dusk, little natural light

Yes

3335

.zip
[2368 MB]

000015

July 19 - 21:37 EDT

dusk, little natural light

Yes

3710

.zip
[2611 MB]

000016

July 19 - 21:48 EDT

dusk, dark

Yes

3634

.zip
[2537 MB]

000017

July 19 - 21:58 EDT

nighttime, dark

Yes

3907

.zip
[2723 MB]

000018

July 19 - 22:28 EDT

nighttime, dark

Yes

3701

.zip
[2586 MB]

000019

July 19 - 22:40 EDT

nighttime, dark

Yes

3718

.zip
[2597 MB]

000020

July 20 - 04:29 EDT

nighttime, dark

No

4042

.zip
[2837 MB]

000021

July 20 - 05:10 EDT

dawn, little natual light

No

3754

.zip
[2679 MB]

000022

July 20 - 05:23 EDT

dawn, little natual light

No

2761

.zip
[1941 MB]

000023

July 20 - 05:34 EDT

dawn, light sky

No

1274

.zip
[881 MB]

000024

July 20 - 05:48 EDT

dawn, light sky

No

964

.zip
[671 MB]

000025

July 20 - 06:04 EDT

dawn, light sky

Yes

1012

.zip
[711 MB]

000026

July 20 - 06:16 EDT

dawn, light sky

Yes

1111

.zip
[763 MB]

000027

July 20 - 06:31 EDT

dawn, large sun glare

Yes

2613

.zip
[1565 MB]

000028

July 20 - 06:44 EDT

dawn, large sun glare

Yes

1826

.zip
[1187 MB]

000029

July 20 - 07:03 EDT

dawn, large sun glare

Yes

1873

.zip
[1184 MB]

000030

July 20 - 07:19 EDT

dawn, medium shadows, large sun glare

Yes

1716

.zip
[1153 MB]

000031

July 20 - 07:39 EDT

dawn, medium shadows, small sun glare

Yes

1173

.zip
[841 MB]

000032

July 20 - 08:35 EDT

morning, medium shadows, small sun glare

No

1034

.zip
[772 MB]

000033

July 20 - 09:18 EDT

morning, sunny, small shadows

No

1045

.zip
[798 MB]

000034

July 20 - 10:21 EDT

morning, cloudy, small shadows

No

986

.zip
[736 MB]

000035

July 20 - 11:26 EDT

mid-day, sunny, small shadows

No

1080

.zip
[822 MB]

000036

July 20 - 12:29 EDT

mid-day, cloudy, small shadows

No

1071

.zip
[815 MB]

000037

July 20 - 14:01 EDT

afternoon, cloudy, small shadows

No

989

.zip
[752 MB]

000038

July 20 - 15:30 EDT

afternoon, cloudy, small shadows

No

950

.zip
[724 MB]


Multiseason

Run

Start Time

Description

Preview

Number of Frames

Download

000000

January 31 - 12:39 EST

mid-day, overcast, little snow, brown grass

826

.zip
[592 MB]

000001

January 31 - 12:48 EST

mid-day, overcast, little snow, brown grass

827

.zip
[593 MB]

000002

January 31 - 12:55 EST

mid-day, overcast, little snow, brown grass

849

.zip
[612 MB]

000003

January 31 - 15:04 EST

afternoon, overcast, little snow, brown grass

716

.zip
[507 MB]

000004

February 01 - 12:23 EST

mid-day, cloudy, some snow, brown grass

717

.zip
[476 MB]

000005

February 02 - 15:32 EST

afternoon, overcast, some snow, brown grass

702

.zip
[544 MB]

000006

February 02 - 17:19 EST

dusk, overcast, some snow, brown grass

631

.zip
[478 MB]

000007

February 03 - 11:18 EST

morning, sunny, some snow, brown grass

757

.zip
[611 MB]

000008

February 03 - 12:57 EST

mid-day, cloudy, some snow, brown grass

721

.zip
[569 MB]

000009

February 03 - 13:13 EST

mid-day, cloudy, some snow, brown grass

686

.zip
[539 MB]

000010

February 03 - 14:38 EST

afternoon, cloudy, some snow, brown grass

844

.zip
[697 MB]

000011

February 03 - 15:16 EST

afternoon, cloudy, some snow, brown grass

769

.zip
[618 MB]

000012

February 07 - 08:59 EST

morning, overcast, little snow, brown grass

933

.zip
[716 MB]

000013

February 07 - 09:04 EST

morning, overcast, little snow, brown grass

872

.zip
[670 MB]

000014

February 07 - 14:16 EST

afternoon, overcast, little snow, brown grass

1218

.zip
[772 MB]

000015

February 07 - 15:50 EST

afternoon, cloudy, little snow, brown grass

1104

.zip
[704 MB]

000016

February 07 - 16:22 EST

afternoon, cloudy, little snow, brown grass

1315

.zip
[834 MB]

000017

February 07 - 17:17 EST

dusk, cloudy, little snow, brown grass

1138

.zip
[702 MB]

000018

February 08 - 17:20 EST

dusk, cloudy, little snow, brown grass

725

.zip
[557 MB]

000019

February 08 - 17:41 EST

dusk, cloudy, snowing lightly, little snow, brown grass

850

.zip
[642 MB]

000020

February 08 - 17:54 EST

dusk, cloudy, snowing lightly, little snow, brown grass

1640

.zip
[1241 MB]

000021

February 08 - 18:22 EST

nighttime, cloudy, snowing lightly, little snow, brown grass

652

.zip
[472 MB]

000022

February 09 - 15:06 EST

afternoon, cloudy, little snow, brown grass

579

.zip
[472 MB]

000023

February 09 - 16:34 EST

afternoon, sunny, little snow, brown grass

579

.zip
[476 MB]

000024

February 10 - 12:15 EST

mid-day, cloudy, little snow, brown grass

576

.zip
[456 MB]

000025

February 10 - 12:22 EST

mid-day, cloudy, little snow, brown grass

575

.zip
[454 MB]

000026

February 10 - 12:30 EST

mid-day, cloudy, little snow, brown grass

537

.zip
[423 MB]

000027

February 10 - 14:54 EST

afternoon, overcast, some snow, brown grass

592

.zip
[417 MB]

000028

February 10 - 15:06 EST

afternoon, overcast, some snow, brown grass

595

.zip
[422 MB]

000029

February 10 - 16:08 EST

afternoon, overcast, some snow, brown grass

638

.zip
[429 MB]

000030

February 10 - 16:20 EST

afternoon, overcast, some snow, brown grass

730

.zip
[492 MB]

000031

February 10 - 17:25 EST

dusk, overcast, some snow, brown grass

687

.zip
[426 MB]

000032

February 11 - 12:03 EST

mid-day, overcast, some snow, brown grass

641

.zip
[442 MB]

000033

February 11 - 16:18 EST

afternoon, overcast, some snow, brown grass

588

.zip
[433 MB]

000034

February 13 - 14:38 EST

afternoon, overcast, full snow

714

.zip
[481 MB]

000035

February 13 - 15:45 EST

afternoon, cloudy, full snow

636

.zip
[449 MB]

000036

February 13 - 16:55 EST

afternoon, cloudy, full snow, small sun glare

652

.zip
[466 MB]

000037

February 13 - 17:09 EST

afternoon, cloudy, full snow, small sun glare

749

.zip
[540 MB]

000038

February 13 - 17:47 EST

dusk, cloudy, full snow

650

.zip
[448 MB]

000039

February 13 - 18:02 EST

dusk, cloudy, full snow

605

.zip
[387 MB]

000040

February 13 - 18:11 EST

dusk, cloudy, full snow

640

.zip
[367 MB]

000041

February 13 - 18:31 EST

nighttime, cloudy, full snow

702

.zip
[384 MB]

000042

February 14 - 11:38 EST

mid-day, cloudy, full snow

650

.zip
[370 MB]

000043

February 14 - 12:17 EST

mid-day, cloudy, full snow

845

.zip
[338 MB]

000044

February 14 - 13:33 EST

mid-day, cloudy, full snow

611

.zip
[367 MB]

000045

February 14 - 14:01 EST

afternoon, cloudy, full snow

598

.zip
[368 MB]

000046

February 15 - 14:03 EST

afternoon, cloudy, full snow

577

.zip
[361 MB]

000047

February 15 - 15:20 EST

afternoon, overcast, some snow, brown grass

589

.zip
[424 MB]

000048

February 16 - 11:46 EST

mid-day, sunny, some snow, brown grass, partial camera obstruction

625

.zip
[311 MB]

000049

February 16 - 14:23 EST

afternoon, sunny, some snow, brown grass

577

.zip
[366 MB]

000050

February 16 - 15:11 EST

afternoon, sunny, some snow, brown grass

571

.zip
[411 MB]

000051

February 16 - 17:11 EST

dusk, sunny, some snow, brown grass, medium sun glare

615

.zip
[455 MB]

000052

February 17 - 13:43 EST

mid-day, sunny, some snow, brown grass

575

.zip
[354 MB]

000053

February 18 - 13:38 EST

mid-day, sunny, some snow, brown grass, muddy

580

.zip
[450 MB]

000054

February 18 - 15:12 EST

afternoon, cloudy, little snow, brown grass, muddy

582

.zip
[480 MB]

000055

February 18 - 16:08 EST

afternoon, sunny, little snow, brown grass, muddy

573

.zip
[474 MB]

000056

February 18 - 17:32 EST

dusk, cloudy, little snow, brown grass, muddy

581

.zip
[422 MB]

000057

February 19 - 13:12 EST

mid-day, sunny, little snow, brown grass, muddy

588

.zip
[509 MB]

000058

February 19 - 15:13 EST

afternoon, sunny, little snow, brown grass, muddy

600

.zip
[525 MB]

000059

February 19 - 16:14 EST

afternoon, sunny, brown grass, small sun glare, muddy

614

.zip
[516 MB]

000060

February 21 - 13:18 EST

mid-day, overcast, brown grass, muddy

592

.zip
[492 MB]

000061

February 21 - 13:25 EST

mid-day, overcast, brown grass, muddy

591

.zip
[491 MB]

000062

February 22 - 13:00 EST

mid-day, overcast, brown grass, muddy

592

.zip
[491 MB]

000063

February 24 - 14:58 EST

afternoon, sunny, brown grass, muddy

588

.zip
[494 MB]

000064

February 27 - 14:10 EST

afternoon, sunny, brown grass

594

.zip
[525 MB]

000065

February 27 - 18:04 EST

dusk, overcast, brown grass

582

.zip
[432 MB]

000066

February 28 - 09:49 EST

morning, overcast, brown grass

583

.zip
[488 MB]

000067

February 28 - 11:34 EST

mid-day, overcast, brown grass

568

.zip
[478 MB]

000068

March 03 - 12:23 EST

mid-day, cloudy, brown grass

588

.zip
[514 MB]

000069

March 03 - 12:28 EST

mid-day, cloudy, brown grass

581

.zip
[503 MB]

000070

March 07 - 13:53 EST

mid-day, overcast, brown grass, muddy

578

.zip
[454 MB]

000071

March 08 - 13:39 EST

mid-day, overcast, brown grass

583

.zip
[474 MB]

000072

March 08 - 15:14 EST

afternoon, sunny, brown grass, small sun glare

577

.zip
[497 MB]

000073

March 10 - 13:07 EST

mid-day, cloudy, brown grass

584

.zip
[504 MB]

000074

March 13 - 12:54 EDT

mid-day, overcast, snowing lightly, little snow, brown grass

529

.zip
[443 MB]

000075

March 13 - 14:29 EDT

afternoon, overcast, snowing lightly, some snow, brown grass

571

.zip
[453 MB]

000076

March 13 - 15:35 EDT

afternoon, overcast, some snow, brown grass

569

.zip
[456 MB]

000077

March 13 - 17:35 EDT

afternoon, overcast, some snow, brown grass

572

.zip
[466 MB]

000078

March 14 - 15:23 EDT

afternoon, overcast, some snow, brown grass

575

.zip
[408 MB]

000079

March 14 - 18:53 EDT

dusk, overcast, some snow, brown grass

578

.zip
[419 MB]

000080

March 15 - 18:52 EDT

dusk, sunny, some snow, brown grass, small sun glare

567

.zip
[443 MB]

000081

March 17 - 11:02 EDT

morning, sunny, little snow, brown grass, muddy

564

.zip
[468 MB]

000082

March 17 - 11:12 EDT

morning, sunny, little snow, brown grass, muddy

590

.zip
[494 MB]

000083

March 17 - 15:12 EDT

afternoon, cloudy, little snow, brown grass, muddy

583

.zip
[492 MB]

000084

March 17 - 17:27 EDT

afternoon, cloudy, little snow, brown grass, muddy

566

.zip
[459 MB]

000085

March 21 - 15:18 EDT

afternoon, sunny, brown grass

594

.zip
[525 MB]

000086

March 21 - 15:27 EDT

afternoon, sunny, brown grass

588

.zip
[518 MB]

000087

March 21 - 19:40 EDT

dusk, sunny, brown grass

591

.zip
[428 MB]

000088

March 23 - 12:36 EDT

mid-day, sunny, brown grass

591

.zip
[518 MB]

000089

March 27 - 16:10 EDT

afternoon, overcast, brown grass

587

.zip
[489 MB]

000090

March 27 - 16:18 EDT

afternoon, overcast, brown grass

587

.zip
[491 MB]

000091

March 27 - 16:23 EDT

afternoon, overcast, brown grass

589

.zip
[483 MB]

000092

March 27 - 16:28 EDT

afternoon, overcast, brown grass

588

.zip
[485 MB]

000093

March 29 - 13:55 EDT

mid-day, overcast, brown grass

592

.zip
[499 MB]

000094

March 29 - 14:01 EDT

mid-day, overcast, brown grass

587

.zip
[489 MB]

000095

March 31 - 09:44 EDT

morning, overcast, some snow, brown grass

578

.zip
[444 MB]

000096

March 31 - 11:41 EDT

mid-day, overcast, little snow, brown grass, muddy

557

.zip
[420 MB]

000097

April 03 - 13:29 EDT

mid-day, overcast brown grass

525

.zip
[443 MB]

000098

April 04 - 13:42 EDT

mid-day, overcast brown grass, muddy

536

.zip
[423 MB]

000099

April 06 - 17:49 EDT

afternoon, raining, green grass, muddy

590

.zip
[468 MB]

000100

April 07 - 12:21 EDT

mid-day, overcast, some snow, muddy

596

.zip
[455 MB]

000101

April 07 - 14:01 EDT

mid-day, cloudy, some snow, green grass muddy

576

.zip
[475 MB]

000102

April 07 - 14:07 EDT

mid-day, cloudy, some snow, green grass muddy

578

.zip
[486 MB]

000103

April 10 - 16:19 EDT

afternoon, overcast, green grass, muddy

579

.zip
[469 MB]

000104

April 10 - 17:43 EDT

afternoon, sunny, green grass, muddy, small sun glare

586

.zip
[493 MB]

000105

April 10 - 17:49 EDT

afternoon, sunny, green grass, muddy, small sun glare

587

.zip
[495 MB]

000106

April 11 - 18:36 EDT

dusk, cloudy, green grass, muddy

571

.zip
[452 MB]

000107

April 13 - 16:42 EDT

afternoon, sunny, green grass, small sun glare

580

.zip
[505 MB]

000108

April 13 - 16:47 EDT

afternoon, sunny, green grass, small sun glare

576

.zip
[497 MB]

000109

April 17 - 16:52 EDT

afternoon, sunny, green grass, small sun glare

579

.zip
[516 MB]

000110

April 17 - 19:53 EDT

dusk, sunny, green grass

573

.zip
[429 MB]

000111

April 18 - 14:04 EDT

afternoon, sunny, green grass

571

.zip
[514 MB]

000112

April 18 - 14:09 EDT

afternoon, sunny, green grass

570

.zip
[513 MB]

000113

April 18 - 16:59 EDT

afternoon, sunny, green grass

577

.zip
[511 MB]

000114

April 18 - 17:12 EDT

afternoon, sunny, green grass, small sun glare

573

.zip
[504 MB]

000115

April 18 - 17:19 EDT

afternoon, sunny, green grass, small sun glare

578

.zip
[506 MB]

000116

April 20 - 15:12 EDT

afternoon, overcast, green grass

576

.zip
[476 MB]

000117

April 21 - 13:28 EDT

mid-day, overcast, green grass

567

.zip
[472 MB]

000118

April 21 - 13:34 EDT

mid-day, overcast, green grass

568

.zip
[475 MB]

000119

April 24 - 17:39 EDT

afternoon, cloudy, green grass, small sun glare

575

.zip
[487 MB]

000120

April 25 - 15:35 EDT

afternoon, cloudy, green grass

563

.zip
[464 MB]

000121

April 25 - 15:40 EDT

afternoon, cloudy, green grass

560

.zip
[461 MB]

000122

April 27 - 11:24 EDT

mid-day, sunny, green grass

568

.zip
[498 MB]

000123

April 27 - 11:29 EDT

mid-day, sunny, green grass

560

.zip
[490 MB]

000124

May 03 - 14:10 EDT

afternoon, sunny, green grass, tall grass

576

.zip
[515 MB]

000125

May 03 - 15:44 EDT

afternoon, sunny, green grass, tall grass

576

.zip
[515 MB]

000126

May 03 - 18:31 EDT

afternoon, sunny, green grass, tall grass, small sun glare

587

.zip
[489 MB]

000127

May 04 - 11:08 EDT

morning, cloudy, green grass, tall grass

570

.zip
[469 MB]

000128

May 05 - 18:06 EDT

afternoon, overcast, green grass, tall grass, muddy

569

.zip
[448 MB]

000129

May 08 - 13:03 EDT

mid-day, sunny, green grass, tall grass, dandelions

586

.zip
[522 MB]

000130

May 08 - 13:08 EDT

mid-day, sunny, green grass, tall grass, dandelions

577

.zip
[515 MB]

000131

May 08 - 17:27 EDT

afternoon, cloudy, green grass, tall grass, dandelions

560

.zip
[460 MB]

000132

May 09 - 13:14 EDT

mid-day, cloudy, green grass, tall grass, dandelions

576

.zip
[496 MB]

000133

May 09 - 13:18 EDT

mid-day, cloudy, green grass, tall grass, dandelions

569

.zip
[471 MB]

000134

May 11 - 14:03 EDT

afternoon, cloudy, green grass, tall grass, dandelions

559

.zip
[460 MB]

000135

May 11 - 15:12 EDT

afternoon, cloudy, green grass, tall grass, dandelions

558

.zip
[459 MB]



License

MIT License

Copyright (c) 2020 Autonomous Space Robotics Lab at the University of Toronto

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.