# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 1 Introduction  
SLAM refers to the model in which a subject equipped with some sensors creates a map of the surroundings during its motion and estimates its own velocity without any a priori information about the environment [1]. SLAM technology is widely used in the fields of autonomous driving, drones, and robot navigation. It can help devices determine their own position and the terrain and obstacles of the surrounding environment. Or, combined with a variety of information, such as semantic information, and perceptual systems, such as target recognition [2], instance segmentation [3], etc., more complex tasks can also be accomplished by SLAM technology. SLAM is mainly divided into laser SLAM and visual SLAM because of the different sensors it carries. In terms of theory and engineering, laser SLAM is more mature, and it has practical applications in industrial industries. The SLAM system called Visual SLAM (VSLAM) relies heavily on pictures to provide environment-aware data. Visual SLAM, which primarily focuses on calculating camera poses and constructing 3D maps using the multi-view geometry approach, is still in the laboratory research stage and has fewer practical applications. However, compared to laser sensors that can only provide single spatial structure perception information, visual sensors have great advantages and the potential to improve inter-frame estimation accuracy and loop closing detection accuracy with their abundant sense information, which includes texture and color [4]. Moreover,cameras are inexpensive and can provide rich environmental information, so VSLAM solutions where a camera serves as the primary sensor are extremely intriguing. The processing of SLAM systems is frequently divided into two phases: front-end inter-frame estimation and back-end optimization. In order to accomplish feature point tracking and mapping in parallel, PTAM [5], which distinguishes between the front-end and back-end, was the first to propose and implement this processing. Front-end inter-frame estimation is based on the sensor information between two adjacent frames to obtain an estimate of the motion during that time interval, also known as visual odometry. There are two categories of inter-frame estimation implementation techniques in the front-end: direct methods and feature point methods. The direct method makes direct use of image information, which relies on dense pixel points to estimate structure and motion directly by minimizing an error measure that is based on the image's pixel-level intensities [6]. However, because the approach is dependent on the assumption of grayscale invariance, it cannot be applied to scenes with significant illumination changes and can lead to tracking failure if the movement of the camera is too large. The feature point method extracts a collection of salient image characteristics (such as corner points) from every image, matches them in consecutive frames using invariant feature descriptors of this set, and then uses the epipolar geometry to steadily recover camera motion and structure. In back-end optimization, camera poses and maps are optimized by minimizing feature point reprojection errors. In loop closing detection, feature correspondence with old landmarks is established by feature descriptors, which increases both the constraints in BA (Bundle Adjustment) optimization, which improves the accuracy of the optimized trajectory, and the relocalization capability, resulting in a more robust system. Feature point methods rely on effective feature detectors and descriptors that enable matching images with large variations in illumination and viewpoint. The feature point method calculates camera motion via a part of the image's points. Although the feature point method takes significant amounts of computational resources in the process of calculating descriptors, it has the excellent feature of being insensitive to changes in illumination and dynamic environmental changes.