"SLAM定位与建图技术探究：视觉感知与自动驾驶"

SLAM (Simultaneous Localization and Mapping) is a crucial component in the field of visual perception for autonomous driving. It involves both the process of determining the position and orientation of a camera in real-time (known as localization) and simultaneously creating a map of the environment the camera is traversing. This is achieved through a combination of techniques such as optical flow, stereo vision, visual odometry, and scene flow. One common approach to SLAM is relative continuous-time SLAM, which utilizes cubic B-splines and Gaussian Process Regression to accurately estimate the camera's position and orientation in relation to its surroundings. This method is motivated by the need for high precision and robustness in autonomous driving applications. Another important aspect of SLAM is long-term lidar SLAM, which focuses on maintaining an up-to-date map of the environment and interpreting scene flow information to improve localization accuracy. This involves ongoing map maintenance and the integration of new data to ensure that the map remains current and relevant for navigation purposes. Overall, SLAM plays a vital role in enabling autonomous vehicles to navigate complex environments with confidence and accuracy. By combining sophisticated algorithms and sensor technologies, SLAM provides a reliable solution for building and updating maps while simultaneously determining the vehicle's position in the environment. This capability is essential for ensuring the safety and efficiency of autonomous driving systems in a wide range of real-world scenarios.