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In recent decades, the field of robotic mapping has witnessed widespread research and development in light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) techniques. This paper aims to provide a significant reference for researchers and engineers in robotic mapping.

This paper focused on the research state of LiDAR-based SLAM for robotic mapping as well as a literature survey from the perspective of various LiDAR types and configurations.

This paper conducted a comprehensive literature review of the LiDAR-based SLAM system based on three distinct LiDAR forms and configurations. The authors concluded that multi-robot collaborative mapping and multi-source fusion SLAM systems based on 3D LiDAR with deep learning will be new trends in the future.

To the best of the authors’ knowledge, this is the first thorough survey of robotic mapping from the perspective of various LiDAR types and configurations. It can serve as a theoretical and practical guide for the advancement of academic and industrial robot mapping.

This study aims to achieve superior localization and mapping performance in point cloud degradation scenarios through the effective removal of dynamic obstacles. With the continuous development of various technologies for autonomous vehicles, the LIDAR-based Simultaneous localization and mapping (SLAM) system is becoming increasingly important. However, in SLAM systems, effectively addressing the challenges of point cloud degradation scenarios is essential for accurate localization and mapping, with dynamic obstacle removal being a key component.

This paper proposes a method that combines adaptive feature extraction and loop closure detection algorithms to address this challenge. In the SLAM system, the ground point cloud and non-ground point cloud are separated to reduce the impact of noise. And based on the cylindrical projection image of the point cloud, the intensity features are adaptively extracted, the degradation direction is determined by the degradation factor and the intensity features are matched with the map to correct the degraded pose. Moreover, through the difference in raster distribution of the point clouds before and after two frames in the loop process, the dynamic point clouds are identified and removed, and the map is updated.

Experimental results show that the method has good performance. The absolute displacement accuracy of the laser odometer is improved by 27.1%, the relative displacement accuracy is improved by 33.5% and the relative angle accuracy is improved by 23.8% after using the adaptive intensity feature extraction method. The position error is reduced by 30% after removing the dynamic target.

Compared with LiDAR odometry and mapping algorithm, the method has greater robustness and accuracy in mapping and localization.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

The purpose of this study is to plan and develop a cost-effective health-care robot for assisting and observing the patients in an accurate and effective way during pandemic situation like COVID-19. The purposed research work can help in better management of pandemic situations in rural areas as well as developing countries where medical facility is not easily available.

It becomes very difficult for the medical staff to have a continuous check on patient’s condition in terms of symptoms and critical parameters during pandemic situations. For dealing with these situations, a service mobile robot with multiple sensors for measuring patients bodily indicators has been proposed and the prototype for the same has been developed that can monitor and aid the patient using the robotic arm. The fuzzy controller has also been incorporated with the mobile robot through which decisions on patient monitoring can be taken automatically. Mamdani implication method has been utilized for formulating mathematical expression of M number of “if and then condition based rules” with defined input X_j (j = 1, 2, ………. s), and output y_i. The inputs and output variables are formed by the membership functions µ_Aij(x_j) and µ_Ci(y_i) to execute the Fuzzy Inference System controller. Here, Aij and Ci are the developed fuzzy sets.

The fuzzy-based prediction model has been tested with the output of medicines for the initial 27 runs and was validated by the correlation of predicted and actual values. The correlation coefficient has been found to be 0.989 with a mean square error value of 0.000174, signifying a strong relationship between the predicted values and the actual values. The proposed research work can handle multiple tasks like online consulting, continuous patient condition monitoring in general wards and ICUs, telemedicine services, hospital waste disposal and providing service to patients at regular time intervals.

The novelty of the proposed research work lies in the integration of artificial intelligence techniques like fuzzy logic with the multi-sensor-based service robot for easy decision-making and continuous patient monitoring in hospitals in rural areas and to reduce the work stress on medical staff during pandemic situation.