In recent years, Simultaneous Localization And Mapping (SLAM) technology has prevailed in a wide range of applications, such as autonomous driving, intelligent robots, Augmented Reality (AR), and Virtual Reality (VR). Multi-sensor fusion using the most popular three types of sensors (e.g., visual sensor, LiDAR sensor, and IMU) is becoming ubiquitous in SLAM, in part because of the complementary sensing capabilities and the inevitable shortages (e.g., low precision and long-term drift) of the stand-alone sensor in challenging environments. In this article, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work. Firstly, a brief introduction of the state estimator formation in SLAM is presented. Secondly, the state-of-the-art algorithms of different multi-sensor fusion algorithms are given. Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations. This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

In this paper, a flexible spacecraft attitude control scheme that guarantees vibration suppression and prescribed performance on transient-state behavior is proposed. Here, parametric uncertainty, external disturbance, unmeasured elastic vibration, actuator saturation, and even configuration misalignment are considered. To guarantee prescribed performance bounds on the transient- and steady-state control errors, a performance constrained control law is formulated with an error transformed function. An elastic modal observer is employed to estimate the unmeasured flexible modal variables, and a command filter is adopted to avoid the tedious analytical computations of time derivatives of virtual control inherent in the control design. Subsequently, a novel auxiliary system is developed to compensate the adverse effects of the actuator saturation constraints, and a compensation term is integrated into the control law to tackle the configuration misalignment. A comparative simulation study is carried out to illustrate the effectiveness and advantages of the proposed approach.