Simultaneous localization and mapping navigation of unmanned ground vehicle based on second-order smooth variable structure filter with improved technique to combat fading for advanced wireless communications

Abstract

Simultaneous localization and mapping is very essential for autonomous navigation when the mobile robot is navigating in unknown environment without a global positioning system. Various techniques to solve the simultaneous localization and mapping problem have been extensively studied using the combination of low-cost sensors. Most of the work in mobile robotics still consists of finding solutions to problems in data exchange between mobile robot and communication control station, which is a challenging task. In fact, communication systems impose severe constraints in terms of channel capacity and transmission quality, because the transmission channel in communication systems is undergoing at the different physical phenomena like scattering, diffusion and diffraction, which occur interference and multiple path effects in wireless communications, while keeping these effects levels low. This article describes a simultaneous localization and mapping problem based on second-order smooth variable structure filter embedded in mobile robot equipped with a sensor for data wireless collection. The inclusion of the control in environments outside the mobile robot field of view can make the wireless communication simultaneous localization and mapping process much more difficult to find a solution under realistic conditions. In order to solve the simultaneous localization and mapping issue and to mitigate the fading phenomena, which affect the quality of service in advanced wireless communication systems, we use a new approach to combat the fading effect without requiring any statistical knowledge of the propagation channel parameters. Several experiments have been done in real-world applications, and good performances were obtained using a second-order smooth variable structure filter–simultaneous localization and mapping algorithm–based wireless communication.

Cross-scene loop-closure detection with continual learning for visual simultaneous localization and mapping

“…SVSF-SLAM is robust face parameters uncertainties and error modeling, thus SVSF-SLAM is very suitable for combining with our method to improve the robustness of the SLAM system. 1,2 We will also study more effective human-robot interaction methods based on the similarity between semantic systems and human navigation. In summary, we hope our research can contribute to the further development of intelligent navigation, semantic cognition, and robust localization for mobile robots.…”

“…from the night with bright lights to Pittsburgh with the diverse environment, namely RobotCar-Night ! Pittsburgh; 2. first experienced the two scenes in (1), and then to the sunny noon, that is, RobotCar-Night ! Pittsburgh !…”

“…Simultaneous localization and mapping (SLAM) is a key technology for autonomous mobile robots. 1,2 Visual-based SLAM has become a research hot spot because of the rich information acquired by visual sensors. The amount of training sample data learned by the visual SLAM system is always limited.…”

“…3,4 For example, when the visual SLAM system trained in scene A works in scene B, the catastrophic forgetting problem of the neural network causes a greater suppression of the memory capability of the visual SLAM system. Meanwhile, the visual SLAM lose the memory of the original scene when it learns 1 School of Mechatronic Engineering and Automation, Foshan University, Foshan, China 2 Department of Computer Science, Jinan University, Guangzhou, China map construction in the new environment. With the larger scene spanning, the longer running time, and the more new sample data, the memory capability of the existing visual SLAM system declines more severely.…”

Self-supervised learning of LiDAR odometry based on spherical projection

“…Compared with EKF-SLAM, SVSF-SLAM has the advantages of low computational complexity, high estimation accuracy, and robustness. Mosbah et al 19 described a SLAM method based on second-order smooth variable structure filter embedded in a mobile robot. Compared with the competitors, Mosbah’s method obtained higher positioning accuracy and robustness.…”