The Boreas dataset was collected by driving a repeated route over the course of one year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset includes over 350km of driving data featuring a 128-channel Velodyne Alpha Prime lidar, a 360$^\circ$ Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. Our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at https://www.boreas.utias.utoronto.caComment: Accepted in IJRR as a data pape
The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data...
Accurate perception and awareness of the environment surrounding the automobile is a challenge in au...
Recent developments in mobile phone applications for polling traffic and roadway condition data from...
We have collected an extensive winter autonomous driving data set consisting of over 4TB of data col...
The availability of public datasets with annotated light detection and ranging (LiDAR) point clouds ...
Michigan Tech\u27s unique climatology allows for relatively effortless collection of autonomous vehi...
In an autonomous driving system, perception - identification of features and objects from the enviro...
Accurate and efficient environmental awareness is a fundamental capability of autonomous driving tec...
Unstructured environments present several challenges to autonomous agents such as robots and autonom...
We present results of the second round of data from an autonomous driving surrogate sensor pod mount...
We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the p...
In the context of autonomous driving, vehicles are inherently bound to encounter more extreme weathe...
This work shows and analyzes the LiDAR performance in real-world heavy winter conditions captured in...
Challenges inherent to autonomous wintertime navigation in forests include lack of reliable a Global...
Funding Information: This work was supported in part by Henry Ford Foundation Finland and in part by...
The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data...
Accurate perception and awareness of the environment surrounding the automobile is a challenge in au...
Recent developments in mobile phone applications for polling traffic and roadway condition data from...
We have collected an extensive winter autonomous driving data set consisting of over 4TB of data col...
The availability of public datasets with annotated light detection and ranging (LiDAR) point clouds ...
Michigan Tech\u27s unique climatology allows for relatively effortless collection of autonomous vehi...
In an autonomous driving system, perception - identification of features and objects from the enviro...
Accurate and efficient environmental awareness is a fundamental capability of autonomous driving tec...
Unstructured environments present several challenges to autonomous agents such as robots and autonom...
We present results of the second round of data from an autonomous driving surrogate sensor pod mount...
We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the p...
In the context of autonomous driving, vehicles are inherently bound to encounter more extreme weathe...
This work shows and analyzes the LiDAR performance in real-world heavy winter conditions captured in...
Challenges inherent to autonomous wintertime navigation in forests include lack of reliable a Global...
Funding Information: This work was supported in part by Henry Ford Foundation Finland and in part by...
The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-4 work was focused on collecting data...
Accurate perception and awareness of the environment surrounding the automobile is a challenge in au...
Recent developments in mobile phone applications for polling traffic and roadway condition data from...