KITTI

time

2011

sensor

Raw (unsynced+unrectified) and processed (synced+rectified) grayscale stereo sequences (0.5 Megapixels, stored in png format)
Raw (unsynced+unrectified) and processed (synced+rectified) color stereo sequences (0.5 Megapixels, stored in png format)
3D Velodyne point clouds (100k points per frame, stored as binary float matrix)
3D GPS/IMU data (location, speed, acceleration, meta information, stored as text file)
Calibration (Camera, Camera-to-GPS/IMU, Camera-to-Velodyne, stored as text file)
3D object tracklet labels (cars, trucks, trams, pedestrians, cyclists, stored as xml file)

Waymo

paper

time

2019

sensor

five LiDAR sensors and five high-resolution pinhole cameras

nusense

time

2019

sensor

6个相机、一个LiDAR、5个RADAR。相比KITTI和Waymo，nuScenes提供了360度的相机视野，所以该数据集可以用来做360度场景的多传感器融合。

data

6个相机的分辨率都是1600x900，除了背后的相机FOV为110度，其他的5个相机的FOV为70度，前置相机和侧面的相机的视野中线角度为55度。也就是说相机覆盖了360度，但是会有重叠部分。相机的采集速率是12Hz。

用了一个32线的LiDAR。采集速率是20Hz

camera

Mai dataset

├── bags
├── bin
│   ├── poses
│   └── sequences
│       ├── 00
│       │   └── velodyne
│       ├── 01
│       │   └── velodyne
│       └── 02
│           └── velodyne
├── gt_models
└── ply
    ├── poses
    └── sequences
        ├── 00
        │   └── velodyne
        ├── 01
        │   └── velodyne
        └── 02
            └── velodyne

bags 为点云文件，应该是ground truth

poses为相机的ground truth

V2X-SIM

https://ai4ce.github.io/V2X-Sim/

时间

2022

V2X-Sim: vehicle-to-everything simulation 第一个合成的V2X辅助自动驾驶协同感知数据集

数据介绍

车辆的左前，右前，正前，右后，左后，正后安装RGB/depth/semantic 传感器

车的顶部 LiDAR、Semantic LiDAR、BEV Semantic Camera

车上还有IMU和GNSS传感器

在道路：放置4个RBG 1个LiDAR、Semantic LiDAR、BEV Semantic

Sensor Setup

map

town3、4、5

Newer College

new

a variety of mobile mapping sensors handcarried at typical walking speeds through New College, Oxford for nearly 6.7 km. The dataset uses two different devices made up of commercially available sensors. These datasets contain some challenging sequences such as fast motion, aggressive shaking, rapid lighting change, and textureless surface.