In recent years, simultaneous localization and mapping with the fusion of LiDAR and vision fusion has gained extensive attention in the field of autonomous navigation and environment sensing. However, its limitations in feature-scarce (low-texture, repetitive structure) environmental scenarios and dynamic environments have prompted researchers to investigate the use of combining LiDAR with other sensors, particularly the effective fusion with vision sensors. This technique has proven to be highly effective in handling a variety of situations by fusing deep learning with adaptive algorithms. LiDAR excels in complex environments, with its ability to acquire high-precision 3D spatial information, especially when dealing with complex and dynamic environments with high reliability. This paper analyzes the research status, including the main research results and findings, of the early single-sensor SLAM technology and the current stage of LiDAR and vision fusion SLAM. Specific solutions for current problems (complexity of data fusion, computational burden and real-time performance, multi-scenario data processing, etc.) are examined by categorizing and summarizing the body of the extant literature and, at the same time, discussing the trends and limitations of the current research by categorizing and summarizing the existing literature, as well as looks forward to the future research directions, including multi-sensor fusion, optimization of algorithms, improvement of real-time performance, and expansion of application scenarios. This review aims to provide guidelines and insights for the development of SLAM technology for LiDAR and vision fusion, with a view to providing a reference for further SLAM technology research.

A fiber Bragg grating (FBG)-based ocean temperature and depth sensor structure is proposed. The pressure sensing section employs a secondary sensitization design comprising a piston and the polycarbonate buffer, while the temperature sensing section utilizes an FBG encapsulated within a metal silver tube, accompanied by a temperature compensation structure. Simulation analyses verify the enhanced sensitivity of the proposed configuration. By selecting suitable materials for the piston, metal tube, and polymer, and optimizing the dimensions of key components, the sensitivity of the bare FBG sensor is significantly improved through the combined effects of the piston, polymer, and metal tube. After optimization, the sensor exhibits a pressure sensitivity of 1.33 nm/MPa and a temperature sensitivity of 102.77 pm/°C, meeting the high-precision detection requirements for ocean temperature and depth sensing. The experimental results show that the temperature sensitivity is 109.9 pm/°C within the temperature range of −5~35 °C, and that the pressure sensitivity is 1.63 nm/MPa within the pressure range of 1~10 MPa. These results confirm that the sensor is well-suited for high-precision ocean temperature and depth measurements.

The significance of detecting faint and diminutive space targets cannot be overstated, as it underpins the preservation of Earth’s orbital environment’s safety and long-term sustainability. Founded by the different response characteristics between targets and backgrounds to aberrations, this paper proposes a novel aberration modulation correlation method (AMCM) for dim and small space target detection. By meticulously manipulating the light path using a wavefront corrector via a modulation signal, the target brightness will fluctuate periodically, while the background brightness remains essentially constant. Benefited by the strong correlation between targets’ characteristic changes and the modulation signal, dim and small targets can be effectively detected. Rigorous simulations and practical experiments have validated the remarkable efficacy of AMCM. Compared to conventional algorithms, AMCM boasts a substantial enhancement in the signal-to-noise ratio (SNR) detection limit from 5 to approximately 2, with an area under the precision–recall curve of 0.9396, underscoring its ability to accurately identify targets while minimizing false positives. In essence, AMCM offers an effective method for detecting dim and small space targets and is also conveniently integrated into other passive target detection systems.