# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 2 Related Work
### Image Feature Point  
The concept of image feature points was suggested by [PERSON] firstly [14]. After that, related scholars at home and abroad studied this problem one after another. [PERSON] et al. [15] proposed the Scale-Invariant Feature Transformation (SIFT) algorithm, which extracts feature points with better scale invariance and rotation invariance. Additionally, it may give reliable matching for a variety of affine distortions, 3D perspective changes, noise increases, and illumination changes. However, it has high operational complexity and takes a long time to extract features. In response to the question, ref. [16] suggested Speeded-Up Robust Feature (SURF). This SURF algorithm improves the SIFT algorithm using integral maps, approximate Hessian matrices, and Haar wavelet transformation operations, and although it improves the algorithm's performance, it still takes longer to calculate the feature descriptors and fails to satisfy the SLAM real-time requirements. In 2010, [PERSON] et al. proposed the Feature from Accelerated Segment Test (FAST) algorithm [17], which is fast and can quickly detect corner points in images, but it does not produce multiscale features, has no orientation information, and does not have rotational invariance. In 2011, [PERSON] et al. presented the ORB algorithm [18], which detects feature points using the FAST technique and obtains the feature descriptors of the image by calculating Binary Robust Independent Elementary Features (BRIEF) algorithm, which is more robust to noise. Therefore, the ORB algorithm extracts features more quickly. Under the same conditions, the extraction speed of feature points is faster than SURF and SIFT algorithms, which can fulfill the system's real-time needs, but its robustness is not as good as SIFT, and there is no scale invariance. Therefore, the algorithm is prone to feature point redundancy, and these redundant feature points will lead to mismatches in image matching. The ORB-SLAM is calculated around the ORB feature, which is an excellent compromise between efficiency and accuracy at this stage of computing platforms. And the ORB feature provides descriptors that enable loop closing detection and relocation during a large range of motion. In response to the problems existing in the feature extraction algorithm, many scholars have improved it. Research in [19] improved the original ORB and suggested a better ORB algorithm based on region division, which ensures an improved uniformity of extracted feature points; however, feature point mismatch still exists. [PERSON] et al. proposed the AGAST algorithm [20], which is an adaptive multi-scale fast corner point extraction algorithm and is an improved version of the FAST algorithm. The technique increases feature extraction speed while also having strong scale invariance. Aiming at the impact of illumination changes on feature tracking, ref. [21] used the AKAZE detector to extract feature points after they constructed a color space with constant illumination based on adaptive histogram equalization and dark channel prior theory. This technique increases the accuracy of extracting and matching image feature points in the event of significant changes in illumination. Research in [22] enhanced the feature method of point-line combination and implemented a local adaptive threshold calculation method and a new meshing technique in ORB feature extraction. After meshing, the threshold is calculated separately for each image block, which improves the adaptive ability of feature extraction. Deep learning has advanced quickly in the field of computer vision in recent years. Many scholars have applied deep learning to feature extraction tasks and achieved good results. In 2018, [PERSON], [PERSON] et al. [23] proposed SuperPoint by using deep learning methods for feature extraction. It uses a single network and self-supervised methods to extract keypoints, but its network structure is large and time-consuming. To tackle this issue, [PERSON], [PERSON], et al.