1. Increase the robustness to dynamic scenes (dynamic SLAM)
Traditional visual SLAM algorithms usually assume that the scene is static, which is not applicable to dynamic scenes. In dynamic scenes, the position and attitude of objects will change, which will have a great impact on the accuracy and robustness of visual SLAM algorithms. Therefore, it is very important to study how to detect and track dynamic objects, how to model dynamic objects, and how to integrate the information of dynamic objects into SLAM algorithms.
2. Improve the accuracy and efficiency of algorithms (feature extraction and matching, GPU and FPGA acceleration)
The accuracy and efficiency of the visual SLAM algorithm are very critical, because they directly affect the usability of the algorithm in practical applications. To improve accuracy, it is possible to investigate how to use more powerful feature descriptors, how to achieve more accurate estimation of camera motion, and how to use more advanced optimization algorithms. In order to improve efficiency, you can study how to use GPU acceleration, how to reduce the amount of calculation, and how to use distributed computing and other technologies.
3. Multi-sensor fusion (camera + IMU)
Traditional visual SLAM algorithms usually rely on sensors such as cameras and IMUs, but these sensors have limitations in accuracy and range. Therefore, it is very meaningful to use other sensor information, such as lidar or RGB-D camera, to improve the accuracy and robustness of SLAM algorithms. Multi-sensor fusion can be achieved in many ways, such as methods based on Kalman filter, methods based on factor graphs, methods based on deep learning, etc.
4. The application of deep learning in SLAM (semantic SLAM)
Deep learning has a wide range of applications in the field of computer vision, and its application in SLAM is gradually being explored. For example, deep learning can be used to extract more powerful feature descriptors, how to use deep learning to estimate camera pose or scene depth, etc. In addition, deep learning can also be used for tasks such as scene understanding, object detection, and semantic segmentation, thereby improving the accuracy and robustness of SLAM algorithms.
5. The balance of real-time and robustness (different application scenarios)
Visual SLAM algorithms need to strike a balance between real-time and robustness. In some applications, such as autonomous navigation and robotic manipulation, real-time is very important, while in others, such as virtual reality, accuracy and stability are more important. Therefore, improving the algorithm to balance real-time and robustness is a very important research direction. This can be achieved by optimizing algorithms, improving sensor design, using suitable hardware, etc.
