New Dataset: Adverse Weather-Augmented Kitti 360 This dataset enhances the KITTI 360 dataset by adding simulated snow, rain, and fog effects to the original clear-weather scans captured by the Velodyne HDL-64 LiDAR sensor. This allows researchers to evaluate their algorithms for tasks like lidar odometry, SLAM, navigation, and 3D object detection under challenging weather conditions. Scenarios: 9 long-term driving sequences with post-processed GNSS/IMU ground truth localization data. Data per Scan: Points include x, y, z coordinates, intensity, ring/channel information, and a normalized azimuth angle representing time. Weather Masks: Each scan includes a mask that identifies points affected by simulated weather (signal attenuation). Augmented Scans: Three compressed versions (rain, fog, and snow) are provided for each scenario, encoded as binary float32. Original Scans: Downloadable from the KITTI-360 dataset. Data Access: A C++/Python code example demonstrates how to read the data format. This dataset provides a valuable resource for researchers developing robust algorithms that function effectively in adverse weather conditions.

2024