Autonomous navigation methods using artificial landmarks (e.g. magnetic tapes or makers) have been proposed [1].
In contrast, autonomous navigation methods, which do not depend on artificial landmarks, have been recently proposed.
The rapid advances in sensing, battery, and aeronautics technologies, together with autonomous navigation methods and equipped low-cost digital cameras have helped make UAVs more affordable, reliable, and easy to operate (Liu et al. 2014).
A new autonomous celestial navigation method for the lunar rover is presented in this paper, which uses the augmented state unscented particle filter (ASUPF) to deal with the systematic error and random error in the measurements.
Third, some proposed autonomous navigation and guidance methods including hazard detection and avoidance for lunar soft landing are analyzed.
As the continued work, this paper mainly analyzes the observability of the autonomous navigation system based on this method, and gives an observability proof for a decomposed subsystem of the autonomous navigation system.
Autonomous navigation by stellar refraction is a type of autonomous celestial navigation method that uses high-accuracy star sensors instead of Earth sensors to provide information regarding Earth's horizon.
Our proposed method could solve the problem and achieved precise autonomous navigation in dynamic outdoor environments, since used DCS can select the navigator, which precisely navigates the robot on the desired path.
Motion planning and autonomous navigation were based on the particle filter-based localization method and the adaptive (or KLD-sampling) Monte Carlo localization approach [22].
As can be seen from Figure 7, only the proposed method and the MNPC could complete the autonomous navigation.
