Background: SLAM plays an important role in the navigation of robots, unmanned aerial vehicles, and unmanned vehicles. The positioning accuracy will affect the accuracy of obstacle avoidance. The quality of map construction directly affects the performance of subsequent path planning and other algorithms. It is the core algorithm of the intelligent mobile application. Therefore, robot vision SLAM has great research value and will be an important research direction in the future.
Objective: By reviewing the latest development and patent of Computer Vision SLAM, this paper provides references to researchers in related fields.
Methods: Computer Vision SLAM patents and literature were analyzed from the aspects of the algorithm, innovation, and application. Among them, there are more than 30 patents and nearly 30 pieces of literature in the past ten years.
Results: This paper reviews the research progress of robot visual SLAM in the last 10 years, summarizes its typical features, especially describes the front part of the visual SLAM system in detail, describes the main advantages and disadvantages of each method, analyses the main problems in the development of robot visual SLAM, prospects its development trend, and finally discusses the related products and patents research status and future of robot visual SLAM technology.
Conclusion: The Robot Vision SLAM can compare the texture information of the environment and identify the difference between the two environments, thus improving accuracy. However, the current SLAM algorithm is easy to fail in fast motion and highly dynamic environments, most SLAM action plans are inefficient, and the image features of VSLAM are too distinguishable. Furthermore, more patents on the Robot Vision SLAM should also be invented.
Washington, DC, USA [http://dx.doi.org/10.1109/ICCV.2003.1238654]
Pasadena, California, USA [http://dx.doi.org/10.1109/ROBOT.2008.4543780]
Barcelona, Spain [http://dx.doi.org/10.1109/ICCV.2011.6126544]
[http://dx.doi.org/10.1109/TIP.2016.2607425] [PMID: 27623586] [http://dx.doi.org/10.1109/TPAMI.2017.2658577] [PMID: 28422651]
Nice, France [http://dx.doi.org/10.1145/3343031.3350539]
Petersburg, Russia [http://dx.doi.org/10.1007/978-3-642-39402-7_14]
Munich, Germany [http://dx.doi.org/10.1007/978-3-030-01246-5_15]