# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 2 Related Work  
### Visual SLAM  
Currently, there are many excellent and mature solutions for SLAM tasks based on the direct method. The DTAM system was presented by [PERSON] et al. in 2011 [PERSON] et al. (2011). Instead of using feature extraction, this system relies on dense pixel points for real-time tracking and map building, comparing the input image with the synthetic view, and then calculating the difference between the image and the view to accomplish localization tracking. However, since this system is a monocular system, it needs to calculate the depth information of each pixel point, so it is computationally expensive. In 2014, ref. [8] suggested LSD-SLAM. This system randomly sets the initial depth value for each pixel point, reconstructs the map for regions with significant gradient changes, and also uses the synthetic view generated by the map to optimize the estimated camera motion. However, it is tough to accurately calculate the depth information of the whole image because it is not possible to estimate regions without texture. In 2017, [PERSON] et al. proposed a completely straightforward system called DSO [9]. This system divides each picture into blocks and selects high-intensity feature points as candidates for reconstructing the map. Simultaneously, it uses geometric and photometric cameras to eliminate the error factor and calibrate the results to decrease the cumulative error. Tested on a publicly available dataset, the system achieves high localization accuracy and is one of the better algorithms in the current direct method.  
While the feature point method was initially used in VSLAM systems in 2007, the literature [1] presented the MonoSLAM system as the first monocular VSLAM system that could run in real-time. To track extremely sparse feature points at the front-end, the system employs an extended Kalman filter as the back-end. In 2009, ref. [5] suggested the monocular SLAM system PTAM. This system was the first to parallelize the tracking and map-building processes. PTAM was the first system to use nonlinear optimization at the back-end instead of traditional filters, and it proposed the keyframes mechanism so that it does not have to process each image finely but instead processes a smaller number of key images to optimize its trajectory and map. Following PTAM, VSLAM research increasingly shifted to the back-end, which became dominated by nonlinear optimization.  
The monocular ORB-SLAM system put forward by [10] represented a peak of mainstream feature point SLAM in 2015. A year later, the authors [11] proposed the ORB-SLAM2 system to address the drawback that ORB-SLAM can only use monocular sensors. ORB-SLAM2 was the first complete the open-source SLAM scheme for monocular, stereo, and RGB-D cameras. And since then, many excellent visual SLAM systems have been improved and optimized as a foundation for ORB-SLAM2.  
Research in [12] proposed the feature-based, tightly coupled VIO system ORB-SLAM3, which builds on ORB-SLAM2 to also conduct visual, visual inertial, and multi-map SLAM utilizing pinhole and fisheye lens models. OV2 SLAM [13] is a fully online and versatile real-time visual SLAM system suggested in 2021 by [PERSON] and collaborators. The system restricts the extraction of features to keyframes and tracks them in the following frames using minimized photometric errors, significantly reducing the computing effort. And it integrates an online BoW method that creates its vocabulary tree step-by-step using feature descriptors computed from keyframes, which can help it adapt to the environment better. The system achieves better accuracy and real-time performance.