Stereo Visual Localization Dataset Featuring Event Cameras

Visual odometry and SLAM methods are facing increasingly complex scenarios and novel solutions are needed to offer more accurate and reliable results in challenging environments. Standard cameras are challenged under low light conditions or very high-speed motion, as they suffer from motion blur and operate at a limited frame rate. These problems can be alleviated by using event cameras - asynchronous visual sensors that offer complementary advantages compared to standard cameras, as they do not suffer from motion blur and support high dynamic range. Although there are a number of existing datasets intended for visual odometry and SLAM that contain event data, most of them are collected using monocular sensors and limited either in terms of camera resolution or ground truth availability. Our work aims to complement this by further supporting the development of robust stereo visual odometry and SLAM algorithms, allowing to exploit both event data and intensity images. We provide both indoor sequences with 6-DoF motion and outdoor vehicle driving sequences that additionally contain 3D lidar data. All sequences contain data from a synchronized high-resolution stereo event and standard cameras, whereas ground truth trajectories are provided by either a motion capture system or a highly accurate GNSS/INS and AHRS that combines the fibre-optic gyro IMU with a dual antenna RTK GNSS receiver.