Research/ PLANETARY ROBOTICS
A new architecture for simultaneous localization and mapping: an application of a planetary rover
Kuo-Kun Tseng, Jun Li, Yachin Chang, Kai Leung Yung, C. Y. Chan, Chih‐Yu Hsu
Perception & State EstimationOpenAlex topic: Robotics and Sensor-Based Localization · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics
A new architecture implements one Monocular Simultaneous Localization and Mapping (SLAM) system to track the unconstraint motion of a mobile robot. The modified ORB (Oriented FAST and Rotated BRIEF) features represent the landmarks for designing a grid feature detection algorithm. An upgraded feature matching method has improved the robustness of feature matching. The Modified coVariance Extended Kalman Filter (MVEKF) estimates the multiple dimension states of the free moving visual sensor instead of the familiar Extended Kalman Filter (EKF). The simulation navigation of Lunar and Mars surfaces proves that the proposed method is robust and efficient.