Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion.

Underwater simultaneous localization and mapping (SLAM) is essential for effectively navigating and mapping underwater environments; however, traditional SLAM systems have limitations due to restricted vision and the constantly changing conditions of the underwater environment. This study thoroughly examined the underwater SLAM technology, particularly emphasizing the incorporation of deep learning methods to improve performance. We analyzed the advancements made in underwater SLAM algorithms. We explored the principles behind SLAM and deep learning techniques, examining how these methods tackle the specific difficulties encountered in underwater environments. The main contributions of this work are a thorough assessment of the research into the use of deep learning in underwater image processing and perception and a comparison study of standard and deep learning-based SLAM systems. This paper emphasizes specific deep learning techniques, including generative adversarial networks (GANs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and other advanced methods to enhance feature extraction, data fusion, scene understanding, etc. This study highlights the potential of deep learning in overcoming the constraints of traditional underwater SLAM methods, providing fresh opportunities for exploration and industrial use.

This study evaluated the physiological and molecular mechanisms underlying cold stress tolerance in golden pompano ( Trachinotus ovatus ) after a 56‐day feeding trial with high‐lipid diets (18%) supplemented with varying methionine (Met) levels (1.04%, 1.14%, 1.24%, 1.34%, 1.44%, 1.54%, and 1.64%). After the feeding period, fish fed 1.24% Met (HLM3) exhibited the highest thermal growth coefficient (TGC) and relative growth rate (RGR), together with the lowest feed conversion ratio (FCR) among all treatments ( p < 0.05). Survival remained 100% across all dietary groups throughout the feeding period. Afterwards, 10 fish from each cage selected randomly were subjected to a 96‐h cold stress from 19.30 ± 0.06°C to 14.38 ± 0.01°C to assess survival, lipid metabolism, antioxidant capacity, immune function, ferroptosis, inflammation, and apoptosis. T. ovatus in the HLM3 group exhibited significantly reduced serum levels of total cholesterol, triglycerides, LDL‐C, AST, ALT, and NEFAs, accompanied by elevated antioxidant enzyme activities (SOD, CAT, T‐AOC, and GSH‐Px) and lower MDA and ROS levels ( p < 0.05). Immune parameters (IgM, LZM, ACP, and AKP) were also markedly enhanced in HLM3. Moreover, hepatic Met and cysteine levels were higher in HLM3, while hepatic lipid deposition was suppressed. Lipolytic ( cpt1a , atgl , lpl , and pparα ) and antioxidant genes ( nrf2 , ho-1 , gxp4 , and MnSOD ) were upregulated, along with increased NADP ⁺ /NADPH ratio and downregulation of keap1 . S lc7a11 , bcl2 , and bcl-xl genes were elevated, while ferroptosis and apoptosis markers were alleviated. Overall, dietary inclusion of 1.24% Met in an 18% high‐lipid diet enhanced the nrf2 ‐mediated antioxidant defense, lipid catabolism, and antiferroptotic pathways, thereby improving cold stress resilience in T. ovatus .

This study investigated the effects of high-lipid diets (HLD) with methionine (Met) supplementations on golden pompano (Trachinotus ovatus). T. ovatus (initial weight 82 ± 0.04 g) were fed with a normal lipid control diet (11.30% crude lipid and 1.04% Met, NLM) and HLDs (18% crude lipid) supplemented with varying Met levels (1.04%, 1.14%, 1.24%, 1.34%, 1.44%, 1.54% and 1.64%) namely HLM1, HLM2, HLM3, HLM4, HLM5, HLM6 and HLM7, respectively. After 56 days of feeding trial, the growth performance such as weight gain rate was significantly elevated in the HLM3 (p < 0.05). The liver lipid droplets area, sum of n-3 and n-6 poly-unsaturated fatty acids and fatty acid synthesis genes were elevated in the HLM1 and HLM7, while the genes for lipid breakdown were elevated in the HLM3. Based on the groups NLM, HLM1, and HLM3, the transcriptome sequence data revealed critical associated lipid metabolism, classic antioxidant pathway nrf2 and ferroptosis markers were influenced by Met and higher lipid. The fish livers in HLD groups with lower or higher Met showed the phenomenon of lipid oxidation obviously, while the reactive oxygen species and malondialdehyde were considerably lowered in the liver of fish in group HLM3 (p < 0.05). The hepatic Met was significantly reduced while cysteine was elevated in the HLM3 compared to HLM7 (p < 0.05). Fe and ferroptosis inducers were significantly upregulated in the liver of HLM1 and HLM7. HLM3 elevated anti-ferroptosis and anti-inflammation markers. In conclusion, Met inclusion in the HLD was associated with nrf2/keap1 and critical anti-ferroptosis-related transcriptional responses and regulated lipid metabolism in T. ovatus. The quadratic regression model revealed the optimal dietary Met in the HLD as 1.32%, which will help formulators make more rational and effective use of dietary lipid to support the better growth of golden pompano.