Abstract
Abstract Vision-based SLAM has an outstanding problem is not work when the camera fast motion, or camera operating environment characterized by scarce. Aiming at this problem, this paper proposes a SLAM method of IMU and vision fusion. This article uses a stereo camera to extract the image ORB feature points. During the camera movement, if the number of extracted feature points is less than a certain threshold and the camera movement cannot be estimated or the estimated camera rotation and translation is greater than a certain threshold, the camera pose is estimated by fusing IMU, Otherwise use feature points to estimate camera pose. This paper uses non-linear optimization methods to perform pose estimation of pure feature points and pose estimation of fused IMU, respectively. The experimental results show that binocular vision SLAM with IMU information can estimate the camera pose more accurately.
Highlights
SLAM means that the robot builds a map of the surrounding environment in real time based on sensor data without any prior knowledge, and infers its own positioning based on the map
Aiming at the problems of the above-mentioned visual SLAM system, this paper proposes an algorithm that fuses IMU and SLAM
During the camera movement, if the number of extracted feature points is less than a certain threshold value, the camera movement cannot be estimated or the estimated camera rotation and translation are greater than a certain threshold value, The camera pose is estimated by fusing the IMU, otherwise feature points are still used to estimate the camera pose
Summary
With the development of robot technology, more and more robots are approaching our lives, such as sweeping robots, shopping mall robots, etc. From the 1980s to the present, more and more sensors are used in SLAM, from early sonar, to later 2D/3D lidar, to monocular, binocular, RGBD, ToF and other cameras. Compared to monocular vision's scale uncertainty and pure rotation problems, binocular cameras can directly calculate the pixel depth. The main disadvantage of the camera as a SLAM sensor is that when the camera moves too fast, the camera will blur images, and the camera will not work in a scene with insufficient environmental feature textures and few feature points. At the same time, during the camera movement process, it makes up for the shortcomings of visual SLAM, ensuring the accuracy of the camera pose estimation in the case of fast camera movement and lack of environmental texture
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