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Event-based Vision for VO/VIO/SLAM in Robotics

This is the repositorie that collects the dataset we used in our papers. We also conclude our works in the field of event-based vision. We hope that we can make some contributions for the development of event-based vision in robotics.

If you have any suggestions or questions, do not hesitate to propose an issue.

If you find this repositorie is helpful in your research, a simple star or citation of our works should be the best affirmation for us. 😊

Dataset for Stereo EVIO

This dataset contains stereo event data at 60HZ and stereo image frames at 30Hz with resolution in 346 × 260, as well as IMU data at 1000Hz. Timestamps between all sensors are synchronized in hardware. We also provide ground truth poses from a motion capture system VICON at 50Hz during the beginning and end of each sequence, which can be used for trajectory evaluation. To alleviate disturbance from the motion capture system’s infrared light on the event camera, we add an infrared filter on the lens surface of the DAVIS346 camera. Note that this might cause the degradation of perception for both the event and image camera during the evaluation, but it can also further increase the challenge of our dataset for the only image-based method.

This is a very challenge dataset for event-based VO/VIO, features aggressive motion and HDR scenarios. EVO, ESVO, Ultimate SLAM are failed in most of the sequences. We think that parameter tuning is infeasible, therefore, we suggest the users use same set of parameters during the evaluation. We hope that our dataset can help to push the boundary of future research on event-based VO/VIO algorithms, especially the ones that are really useful and can be applied in practice.

Acquisition Platform

image

The Platform for Data Collection

Driver Installation

We thanks the rpg_dvs_ros for intructions of event camera driver.

We add the function of the hardware synchronized for stereo setup, the source code is available in link. After installing the driver, the user can directly run the following command to run your stereo event camera:

roslaunch stereo_davis_open.launch

Tips: Users need to adjust the lens of the camera, such as the focal length, aperture. Filters are needed for avoiding the interfere from infrared light under the motion capture system. For the dvxplorer, the sensitive of event generation should be set, e.g. bias_sensitivity. Users can visualize the event streams to see whether it is similiar to the edge map of the testing environments, and then fine-tune it. Otherwise, the event sensor would output noise and is useless just like M2DGR.

Data Sequence

In our VICON room:

Sequence Name Collection Date Total Size Duration Features One Drive Baidu Disk
hku_agg_translation 2022-10 3.63g --- aggressive Rosbag Rosbag
hku_agg_rotation 2022-10 3.70g --- aggressive Rosbag Rosbag
hku_agg_flip 2022-10 3.71g --- aggressive Rosbag Rosbag
hku_agg_walk 2022-10 4.52g --- aggressive Rosbag Rosbag
hku_hdr_circle 2022-10 2.91g --- hdr Rosbag Rosbag
hku_hdr_slow 2022-10 4.61g --- hdr Rosbag Rosbag
hku_hdr_tran_rota 2022-10 3.37g --- aggressive & hdr Rosbag Rosbag
hku_hdr_agg 2022-10 4.43g --- aggressive & hdr Rosbag Rosbag
hku_dark_normal 2022-10 4.24g --- dark & hdr Rosbag Rosbag

Outdoor large-scale (outdoor without ground truth):

The path length of this data sequence is about 1866m, which covers the place around 310m in length, 170m in width, and 55m in height changes, from Loke Yew Hall to the Eliot Hall and back to the Loke Yew Hall in HKU campus. That would be a nice travel for your visiting the HKU 😍 Try it!

Sequence Name Collection Date Total Size Duration Features Rosbag
hku_outdoor_large-scale 2022-11 67.4g 34.9minutes Indoor+outdoor; large-scale Rosbag

Dataset for Monocular EVIO

You can use these data sequence to test your monocular EVIO in different resolution event cameras. TheDAVIS346 (346x260) and DVXplorer (640x480)are attached together (shown in Figure) for facilitating comparison. All the sequences are recorded in HDR scenarios with very low illumination or strong illumination changes through switching the strobe flash on and off. We also provide indoor and outdoor large-scale data sequence.

Acquisition Platform

image

The Platform for Data Collection

  • The configuration file is in link

Data Sequence

With VICON as ground truth:

Sequence Name Collection Date Total Size Duration Features One Drive Baidu Disk
vicon_aggressive_hdr 2021-12 23.0g --- HDR, Aggressive Motion Rosbag Rosbag
vicon_dark1 2021-12 10.5g --- HDR Rosbag Rosbag
vicon_dark2 2021-12 16.6g --- HDR Rosbag Rosbag
vicon_darktolight1 2021-12 17.2g --- HDR Rosbag Rosbag
vicon_darktolight2 2021-12 14.4g --- HDR Rosbag Rosbag
vicon_hdr1 2021-12 13.7g --- HDR Rosbag Rosbag
vicon_hdr2 2021-12 16.9g --- HDR Rosbag Rosbag
vicon_hdr3 2021-12 11.0g --- HDR Rosbag Rosbag
vicon_hdr4 2021-12 19.6g --- HDR Rosbag Rosbag
vicon_lighttodark1 2021-12 17.0g --- HDR Rosbag Rosbag
vicon_lighttodark2 2021-12 12.0g --- HDR Rosbag Rosbag

indoor (no ground truth):

Sequence Name Collection Date Total Size Duration Features Rosbag
indoor_aggressive_hdr_1 2021-12 16.62g --- HDR, Aggressive Motion Rosbag
indoor_aggressive_hdr_2 2021-12 15.66g --- HDR, Aggressive Motion Rosbag
indoor_aggressive_test_1 2021-12 17.94g --- Aggressive Motion Rosbag
indoor_aggressive_test_2 2021-12 8.385g --- Aggressive Motion Rosbag
indoor_1 2021-12 3.45g --- --- Rosbag
indoor_2 2021-12 5.31g --- --- Rosbag
indoor_3 2021-12 5.28g --- --- Rosbag
indoor_4 2021-12 6.72g --- --- Rosbag
indoor_5 2021-12 13.79g --- --- Rosbag
indoor_6 2021-12 20.39g --- --- Rosbag

Outdoor (no ground truth):

Sequence Name Collection Date Total Size Duration Features Rosbag
indoor_outdoor_1 2021-12 20.87g --- ****** Rosbag
indoor_outdoor_2 2021-12 39.5g --- ****** Rosbag
outdoor_1 2021-12 5.52g --- ****** Rosbag
outdoor_2 2021-12 5.27g --- ****** Rosbag
outdoor_3 2021-12 6.83g --- ****** Rosbag
outdoor_4 2021-12 7.28g --- ****** Rosbag
outdoor_5 2021-12 7.26g --- ****** Rosbag
outdoor_6 2021-12 5.38g --- ****** Rosbag
outdoor_round1 2021-12 11.27g --- ****** Rosbag
outdoor_round2 2021-12 13.34g --- ****** Rosbag
outdoor_round3 2021-12 37.26g --- ****** Rosbag

On quadrotor platform (sample sequence in our PL-EVIO work):

We also provide the data squences that are collected in the flighting quadrotor platform using DAVIS346.

image

The Platform for Data Collection

  • The configuration file is in link
Sequence Name Collection Date Total Size Duration Features Rosbag
Vicon_dvs_fix_eight 2022-08 1.08g --- quadrotor flighting Rosbag
Vicon_dvs_varing_eight 2022-08 1.48g --- quadrotor flighting Rosbag
outdoor_large_scale1 2022-08 9.38g 16 minutes ****** Rosbag
outdoor_large_scale2 2022-08 9.34g 16 minutes ****** Rosbag

Modified Public Dataset

Modified VECtor Dataset

VECtor dataset covering the full spectrum of motion dynamics, environment complexities, and illumination conditions for both small and large-scale scenarios. We modified the frequency of the event_left and event_right (60Hz) and the message format from "prophesee_event_msgs/EventArray" to "dvs_msgs/EventArray" in the VECtor dataset, so that there is more event information in each frame and we can extract effective point and line features from the event stream. We release this modified VECtor Dataset to facilitate research on event camera. For the convenience of the user, we also fuse the individual rosbag from different sensors together (left_camera, right_camera, left_event, right_event, imu, groundtruth).

image

Overview of Vector dataset

Sequence Name Total Size One Drive Baidu Disk
board-slow 3.18g Rosbag Rosbag
corner-slow 3.51g Rosbag Rosbag
robot-normal 3.39g Rosbag Rosbag
robot-fast 4.23g Rosbag Rosbag
desk-normal 8.82g Rosbag Rosbag
desk-fast 10.9g Rosbag Rosbag
sofa-normal 10.8g Rosbag Rosbag
sofa-fast 6.7g Rosbag Rosbag
mountain-normal 10.9g Rosbag Rosbag
mountain-fast 16.6g Rosbag Rosbag
hdr-normal 7.73g Rosbag Rosbag
hdr-fast 13.1g Rosbag Rosbag
corridors-dolly 7.78g Rosbag Rosbag
corridors-walk 8.56g Rosbag Rosbag
school-dolly 12.0g Rosbag Rosbag
school-scooter 5.91g Rosbag Rosbag
units-dolly 18.5g Rosbag Rosbag
units-scooter 11.6g Rosbag Rosbag

Modified DSEC Dataset

DSEC is a stereo camera dataset in driving scenarios that contains data from two monochrome event cameras and two global shutter color cameras in favorable and challenging illumination conditions. In addition, it also collects Lidar data, IMU and RTK GPS measurements. However, the data sequence of different sensors in DSEC are divided and in different data formats, which is very unfriendly to users. Therefore, we convert them into same rosbag which might be easier for event-based VIO evaluation. The code of processing the data can be also available in here.

image

Overview of DSEC dataset

Sequence Name Total Size One Drive
zurich city 04 (a) 13.8g Rosbag
zurich city 04 (b) 5.33g Rosbag
zurich city 04 (c) 18.7g Rosbag
zurich city 04 (d) 15.5g Rosbag
zurich city 04 (e) 4.94g Rosbag
zurich city 04 (f) 15.1g Rosbag

Our Works in Event-based Vision

1. IROS2022

This work proposed event inertial odometry (EIO). We do not rely on the use of image-based corner detection but design a asynchronously detected and uniformly distributed event-cornerdetector from events-only data. The event-corner features tracker are then integrated into a sliding windows graph-based optimization framework that tightly fuses the event-corner features with IMU measurement to estimate the 6-DoF ego-motion.

video

Demo Video (click the image to open video demo)

@inproceedings{GWPHKU:EIO,
  title={Monocular Event Visual Inertial Odometry based on Event-corner using Sliding Windows Graph-based Optimization},
  author={Guan, Weipeng and Lu, Peng},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={2438-2445},
  year={2022},
  organization={IEEE}
}

2. PL-EVIO

This work proposed the event-based visual-inertial odometry (EVIO) framework with point and line features, including: pruely event (PL-EIO) and event+image (PL-EVIO). It is reliable and accurate enough to provide onboard pose feedback control for the quadrotor to achieve aggressive motion, e.g. flipping.

  • This work is accepted by T-ASE and simultaneously transferred to ICRA2024. PDF can be downloaded here
  • Results (raw trajectories)
  • An extended version of our PL-EVIO: realizing high-accurate 6-DoF pose tracking and 3D semi-dense mapping (monocular event only) can be seen in Link
video

Demo Video (click the image to open video demo)

video

Onboard Quadrotor Flip using Our PL-EVIO (click the gif to open video demo)

@article{GWPHKU:PL-EVIO,
  title={PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features},
  author={Guan, Weipeng and Chen, Peiyu and Xie, Yuhan and Lu, Peng},
  journal={IEEE Transactions on Automation Science and Engineering},
  year={2023}
}

3. ESVIO

This work proposed the first stereo event-based visual inertial odometry framework, including ESIO (purely event-based) and ESVIO (event with image-aided). The stereo event-corner features are temporally and spatially associated through an event-based representation with spatio-temporal and exponential decay kernel. The stereo event tracker are then tightly coupled into a sliding windows graph-based optimization framework for the estimation of ego-motion.

video

Onboard Quadrotor Flight using Our ESVIO as State Estimator (click the gif to open video demo)

@article{GWPHKU:ESVIO,
  title={ESVIO: Event-based Stereo Visual Inertial Odometry},
  author={Chen, Peiyu and Guan, Weipeng and Lu, Peng},
  journal={IEEE Robotics and Automation Letters},
  year={2023},
  volume={8},
  number={6},
  pages={3661-3668},
  publisher={IEEE}
}

4. ECMD

ECMD is an event-based dataset for autonomous driving. It provides data from two sets of stereo event cameras with different resolutions (640x480, 346x260), stereo industrial cameras, an infrared camera, a top-installed mechanical LiDAR with two slanted LiDARs, two consumer-level GNSS receivers, and an onboard IMU. Meanwhile, the ground-truth of the vehicle was obtained using a centimeter-level high-accuracy GNSS-RTK/INS navigation system.

  • The dataset is available at here.
video

Overview of ECMD (click the gif to open video demo)

@article{GWPHKU:ECMD,
  title={ECMD: An Event-Centric Multisensory Driving Dataset for SLAM},
  author={Chen, Peiyu and Guan, Weipeng and Huang, Feng and Zhong, Yihan and Wen, Weisong and Hsu, Li-Ta and Lu, Peng},
  journal={IEEE Transactions on Intelligent Vehicles},
  year={2023}
}

Using Our Methods as Comparison

We strongly recommend the peers to evaluate their proposed method using our dataset, and do the comparison with the raw results from our methods using their own accuracy criterion.

The raw results/trajectories of our methods can be obtained in 👉 here.

Recommendation

LICENSE

This repositorie is licensed under MIT license. International License and is provided for academic purpose. If you are interested in our project for commercial purposes, please contact Dr. Peng LU for further communication.

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