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Visual simultaneous localization and mapping (SLAM) has limitations such as sensitivity to lighting changes and lower measurement accuracy. The effective fusion of information from multiple modalities to address these limitations has emerged as a key research focus. This study aims to provide a comprehensive review of the development of vision-based SLAM (including visual SLAM) for navigation and pose estimation, with a specific focus on techniques for integrating multiple modalities.

This paper initially introduces the mathematical models and framework development of visual SLAM. Subsequently, this paper presents various methods for improving accuracy in visual SLAM by fusing different spatial and semantic features. This paper also examines the research advancements in vision-based SLAM with respect to multi-sensor fusion in both loosely coupled and tightly coupled approaches. Finally, this paper analyzes the limitations of current vision-based SLAM and provides predictions for future advancements.

The combination of vision-based SLAM and deep learning has significant potential for development. There are advantages and disadvantages to both loosely coupled and tightly coupled approaches in multi-sensor fusion, and the most suitable algorithm should be chosen based on the specific application scenario. In the future, vision-based SLAM is evolving toward better addressing challenges such as resource-limited platforms and long-term mapping.

This review introduces the development of vision-based SLAM and focuses on the advancements in multimodal fusion. It allows readers to quickly understand the progress and current status of research in this field.

This paper aims to deal with the force and position tracking problem when a robot performs a task in interaction with an unknown environment and presents a hybrid control strategy based on variable admittance control and fixed-time control.

A hybrid control strategy based on variable admittance control and fixed-time control is presented. Firstly, a variable stiffness admittance model control based on proportional integral and differential (PID) is adopted to maintain the expected force value during the task execution. Secondly, a fixed-time controller based on radial basis function neural network (RBFNN) is introduced to handle the model uncertainties and ensure the fast position tracking convergence of the robot system, while the singularity problem is also avoided by designing the virtual control variable with piecewise function.

Simulation studies conducted on the robot manipulator with two degrees of freedom have verified the superior performance of the proposed control strategy comparing with other methods.

A hybrid control scheme for robot–environment interaction is presented, in which the variable stiffness admittance method is adopted to adjust the interaction force to the desired value, and the RBFNN-based fixed-time position controller without singularity problem is designed to ensure the fast convergence of the robot system with model uncertainty.

The purpose of this paper is to enhance the performance of robots in peg-in-hole assembly tasks, enabling them to swiftly and robustly accomplish the task. It also focuses on the robot’s ability to generalize across assemblies with different hole sizes.

Human behavior in peg-in-hole assembly serves as inspiration, where individuals visually locate the hole firstly and then continuously adjust the peg pose based on force/torque feedback during the insertion process. This paper proposes a novel framework that integrate visual servo and adjustment based on force/torque feedback, the authors use deep neural network (DNN) and image processing techniques to determine the pose of hole, then an incremental learning approach based on a broad learning system (BLS) is used to simulate human learning ability, the number of adjustments required for insertion process is continuously reduced.

The author conducted experiments on visual servo, adjustment based on force/torque feedback, and the proposed framework. Visual servo inferred the pixel position and orientation of the target hole in only about 0.12 s, and the robot achieved peg insertion with 1–3 adjustments based on force/torque feedback. The success rate for peg-in-hole assembly using the proposed framework was 100%. These results proved the effectiveness of the proposed framework.

This paper proposes a framework for peg-in-hole assembly that combines visual servo and adjustment based on force/torque feedback. The assembly tasks are accomplished using DNN, image processing and BLS. To the best of the authors’ knowledge, no similar methods were found in other people’s work. Therefore, the authors believe that this work is original.

This paper aims to investigate the differences in hot corrosion behavior of the GTD222 superalloy and TiC/GTD222 composite in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ at 900°C.

The GTD222 superalloy and TiC/GTD222 nickel-based composite were prepared using selective laser melting (SLM). Subsequently, the hot corrosion behavior of the two alloys was systematically investigated in a salt mixture consisting of 75% Na₂SO₄ and 25% K₂SO₄ (Wt.%) at 900°C.

The TiC/GTD222 composite exhibited better hot corrosion resistance compared to the GTD222 superalloy. First, the addition of alloying elements led to the formation of a protective oxide film on the TiC/GTD222 composites 20 h before hot corrosion. Second, TiC/GTD222 composite corrosion surface has a higher Ti content, after 100 h of hot corrosion, the composite corrosion surface Ti content of 10.8% is more than two times the GTD222 alloy 4% Ti. The Ti and Cr oxides are tightly bonded, effectively resisting the erosion of corrosive elements.

The hot corrosion behavior of GTD222 superalloy and TiC/GTD222 composites prepared by SLM in a mixed salt of 75% Na₂SO₄ and 25% K₂SO₄ was studied for the first time. This study provides insights into the design of high-temperature alloys resistant to hot corrosion.