SLAM for structured environments using mechanically scanned imaging sonar

This paper describes an approach that combines the navigation data given by a Doppler Velocity Logs (DVL), the MTi Motion Reference Unit (MRU) and a Mechanically Scanned Imaging Sonar (MSIS) as a principal sensor to efficiently solve underwater Simultaneous Localization and Mapping (SLAM) problem in structured environments such as marine platforms, harbors, or dams, etc. The MSIS has been chosen of its capacity to produce a rich representation of the environment. In recent years, to solve the SLAM Autonomous Underwater Vehicle (AUV) problem, very few solutions have been proposed. Our contribution has introduced a method based on the Nonlinear H-infinity filter \((NH\infty )\) to solve the SLAM-AUV problem. In this work, the \(NH\infty \)-SLAM algorithm is implemented to construct a map in partially structured environments and localize the AUV within this map. The data-set used in this paper are taken from the experiments carried out in a marina located in the Costa Brava (Spain) with the Ictineu AUV which is necessary to test different SLAM algorithms. The validation of the proposed algorithm through simulation in offline is presented and compared to the EKF-SLAM algorithm. The \(NH\infty \)-SLAM algorithm provides an accurate estimate than EKF-SLAM and good results were obtained.

Unmanned aerial vehicles (UAVs) are being employed in a rapidly increasing number of applications in mining, including the emerging area of mapping underground void spaces such as stopes, which are otherwise inaccessible to humans, automated ground vehicles and survey technologies. Void mapping can provide both visual rock surface and 3D structural information about stopes, supporting more effective planning of ongoing blast designs. Underground stope mapping by UAVs, however, involves overcoming a number of engineering challenges to allow flights beyond operator line-of-sight where there is no global positioning system (GPS), natural or artificial light, or existing communications infrastructure. This paper describes the construction of a UAV sensor suite that uses sound navigation and ranging (SONAR) data to create a rough 3D model of the underground UAV operational environment in real time to provide operators with high situational awareness for beyond line-of-sight operations. The system also provides a backup when dust obscures visual sensors to provide situation awareness and a coarser, but still informative, 3D model of the underground space. Typically, light detection and ranging (LIDAR) systems have superseded SONAR sensors for similar applications. LIDAR is much more accurate than SONAR, but has several disadvantages. SONAR sensor data is sparse, and therefore much easier to process in real time on-board the UAV than LIDAR. The SONAR sensor hardware is also lighter than current LIDAR systems, which is of importance regarding the constrained payload capacity of UAVs. However, the most important factor that makes SONAR stand out in this application is its ability to operate in dusty or smoke-filled environments. The UAV system was tested both above and below-ground using a predefined path with check point locations for the UAV to follow. Due to the lack of GPS, survey points in combination with photogrammetry allowed the UAV’s location to be estimated. This allowed the system to be tested to determine how accurate the SONAR data is in comparison with 3D modelling via photogrammetry of images from a separate digital single-lens reflex camera. Comparing the shape of void surfaces determined by photogrammetry with that determined by SONAR provides quantifiable accuracy when the photogrammetry models are used as ground truth data. Aboveground and underground pilot studies have determined that SONAR sensors provide acceptable accuracy compared with modelling via photogrammetry, sufficient to provide effective situational awareness for human operation of the UAV beyond line-of-sight.

Sound Navigation and Ranging (SONAR) are widely deployed in marine applications, locating submarines, underwater research, weather forecasting, oil and gas exploration, and medical fields. There are three types of SONAR: passive, active, and recently hybrid SONARs. Passive SONAR deals with weak signals at low frequencies (<10kHz), demanding low offset and low flicker noise amplifiers, while active SONAR works at higher frequencies (up to MHz), calling for low thermal noise and wide UGBW amplifiers. Hybrid SONAR covers both low and high-frequency bandwidth, and its amplifier requires all mentioned features. Although the chopping technique is a remedy to reduce the flicker noise in multi-stage amplifiers, the input bias current and the residue offset are proportional to the chopping frequency, $\mathrm{f}_{\mathrm{c}\mathrm{h}}$. Fuithermore, increasing $\mathrm{f}_{\mathrm{c}\mathrm{h}}$ demands for increasing UGBW and power consumption of the amplifier rarely exceed a few hundred kHz and limits its highfrequency applications [1, 2]. This paper proposes an OpAmp with 69MHz UGBW, 40V/us SR at 100pF load cap, 3nV/$\sqrt{Hz}$ input referred thermal noise PSD, lower than 4.5$\mu$V offset voltage, and only 1. 5nA input current at 2. 5MHz$\mathrm{f}_{\mathrm{c}\mathrm{h}}$, while the circuit draws 3. 3mA from 1. 8V. This is made possible by introducing a new compensation scheme, modified biasing circuitry for the rail-rail input $\mathrm{g}_{\mathrm{m}}$ stage, and presenting low input current bootstrap switches.

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation.

In this work, we tested Simultaneous localization and mapping (SLAM) about mobile robots in indoor environment, where all experiments were conducted based on the Robot Operating System (ROS). The urban search and rescue(USAR) environment was build in the ROS simulation tool Gazebo, and our car was used to test hector SLAM in Gazebo. The rplidar A1 single-line lidar was used for 2D laser scan matching data acquisition in the practical experiments and the indoor map was built by using the open source algorithms gmapping, karto SLAM, hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the three open source algorithms show that the mobile robot for simultaneous localization and mapping (SLAM) is feasible, and high-precision grid maps can be constructed.

Underwater detection is accomplished using an underwater ultrasonic sensor, sound navigation and ranging (SONAR). Stealth to avoid detection by SONAR plays a major role in modern underwater warfare. In this study, we propose a smart skin that avoids detection by SONAR via controlling the signal reflected from an unmanned underwater vehicle (UUV). The smart skin is a multilayer transducer composed of an acoustic window, a double-layer receiver, and a single-layer transmitter. It separates the incident signal from the reflected signal from outside through the time-delay separation method and cancels the reflected wave from the phase-shifted transmission sound. The characteristics of the receiving and transmitting sensors were analyzed using a finite element analysis. Three types of devices were compared in the design of the sensors. Polyvinylidene fluoride (PVDF), which had little effect on the transmitted sound, was selected as the receiving sensor. A stacked piezoelectric transducer with high sensitivity compared to a cymbal transducer was used as the transmitter. The active reflection control system was modeled and verified using 2D 360° reflection experiments. The stealth effect that could be achieved by applying a smart skin to a UUV was presented through an active reflection-control omnidirectional reflection model.

Simultaneous localization and mapping (SLAM) is an active localization method for Autonomous Underwater Vehicle (AUV), and it can mainly be used in unknown and complex areas such as coastal water, harbors, and wharfs. This paper presents a practical occupancy grid-based method based on forward-looking sonar for AUV. The algorithm uses an extended Kalman filter (EKF) to estimate the AUV motion states. First, the SLAM method fuses the data coming from the navigation sensors to predict the motion states. Subsequently, a novel particle swarm optimization genetic algorithm (PSO-GA) scan matching method is employed for matching the sonar scan data and grid map, and the matching pose would be used to correct the prediction states. Lastly, the estimated motion states and sonar scan data would be used to update the grid map. The experimental results based on the field data have validated that the proposed SLAM algorithm is adaptable to underwater conditions, and accurate enough to use for ocean engineering practical applications.

Active Bathymetric SLAM for autonomous underwater exploration

Autonomous service robots are gaining popularity as they find application in different sectors such as logistics, hospitality, medical, space exploration and military. It is widely seen that autonomous service robots help increase efficiency, reduce cost and ensure safer operations. Automating material handling operations in highly dynamic indoor environments is risky and involves use of complex algorithms. However, continuously advancing technologies and machine programming tend to overcome these challenges. The three essential functions of an autonomous robot are to identify its instantaneous location in three-dimensional space, generate the environment map and decide an optimal path for locomotion. Simultaneous Localization and Mapping (SLAM) is a widely used algorithm for navigation in unfamiliar environments. This chapter presents a cost-effective autonomous ground robot for automating material handling, transportation and other repetitive tasks in dynamically changing surroundings. The robot is programmed to map the indoor environment and localize itself in the workspace with the help of SLAM and Adaptive Monte Carlo Localization (AMCL). In addition, the robot can manoeuvre through the indoor workspace by avoiding static and dynamic obstacles. This work employs the GMapping open-source package in the Robot Operating System (ROS) for performing SLAM with the aid of a laser range finder, wheel odometry and inertial data. The GMapping algorithm uses a probabilistic approach to construct a map and estimate the robot’s location in two-dimensional space. In addition, this chapter presents an autonomous navigation feature with the help of the AMCL method for localizing the robot within an uncertain environment and making the robot move to its goal using move base actions in ROS. The path planning of the mobile robot is classified into global and local plans. The global plan is responsible for planning the path from the start point to the endpoint. The local plan is responsible for planning the actual movement of the robot by avoiding any dynamic obstacles not mapped earlier. This work uses the Dijistra algorithm for the global and the dynamic window approach (DWA) algorithm for the local path planning. The optimization algorithm minimizes the error between the global and local paths. The simulation study of the proposed algorithm is performed in a virtual environment called Gazebo in ROS, which is nearly similar to a real-time indoor workspace. Further, a robot prototype is fabricated, and its performance is evaluated in an unknown environment. The robot prototype is equipped with brushed DC motors with optical encoder feedback for wheel odometry (ODOM), Light Detection and Ranging (LiDAR), and Inertial Measurement Unit (IMU) sensors controlled using a Jetson Nano. The simulation and experimental results showcase the robot’s successful two-dimensional localization and navigation in the factory environment. The current system has the potential for indoor assistance during material transfer and logistic applications. Further upgrades to the system can make it usable for Automated Storage and Retrieval System (ASRS) applications in warehouses/factories.

We propose a method to improve the geo-referenced accuracy and self-consistency of bathymetric maps generated by Autonomous Underwater Vehicles (AUVs), where the navigation solution is prone to drift when GPS or other methods of absolute positioning are unavailable. This is accomplished using a non-feature based approach to Simultaneous Localization and Mapping (SLAM) that utilizes a 2D grid structure to represent the map and a Distributed Particle Filter to track the uncertainty in the vehicle state. Our method does not need to explicitly identify features in the surrounding environment or apply complicated matching algorithms to our bathymetry, as is commonly done when performing terrain aided navigation. In this work we demonstrate how a prior low-resolution map generated by a surface vessel in a standard gridded form can be readily integrated into our approach to bathymetric SLAM to additionally enforce consistency between the prior map and the AUV bathymetry. We illustrate the proposed approach using data from recent survey work undertaken off the coast of the Tasman Peninsula in South Eastern Tasmania, Australia. The results achieved by the Bathymetric distributed Particle SLAM (BPSLAM) filter are shown to improve the maps and trajectories when compared to dead reckoning fused with USBL observations.

This paper presents the comparison and analysis of LIDAR (Light Detection and Ranging)-based SLAM (Simultaneous Localization and Mapping) Frameworks such as Hector SLAM, GMapping and Karto SLAM in dynamic environments with moving objects. Many experiments have been conducted using a mobile robot equipped with a LIDAR, which is operated on ROS (Robot Operating System) in two indoor environments including a moving object. Experimental results showed that the results of the three SLAM frameworks were different according to the types and trajectories of the moving object.

The purpose of this paper is to offer a unique adaptive path planning framework to address a new challenge known as the Unknown environment Persistent Monitoring Problem (PMP). To identify the unknown events’ occurrence location and likelihood, an unmanned ground vehicle (UGV) equipped with a Light Detection and Ranging (LIDAR) and camera is used to record such events in agriculture land. A certain level of detecting capability must be the distinct monitoring priority in order to keep track of them to a certain distance. First, to formulate a model, we developed an event-oriented modelling strategy for unknown environment perception and the effect is enumerated by uncertainty, which takes into account the sensor’s detection capabilities, the detection interval, and monitoring weight. A mobile robot scheme utilizing LIDAR on integrative approach was created and experiments were carried out to solve the high equipment budget of Simultaneous Localization and Mapping (SLAM) for robotic systems. To map an unfamiliar location using the robotic operating system (ROS), the 3D visualization tool for Robot Operating System (RVIZ) was utilized, and GMapping software package was used for SLAM usage. The experimental results suggest that the mobile robot design pattern is viable to produce a high-precision map while lowering the cost of the mobile robot SLAM hardware. From a decision-making standpoint, we built a hybrid algorithm HSAStar (Hybrid SLAM &amp; A Star) algorithm for path planning based on the event oriented modelling, allowing a UGV to continually monitor the perspectives of a path. The simulation results and analyses show that the proposed strategy is feasible and superior. The performance of the proposed hyb SLAM-A Star-APP method provides 34.95%, 27.38%, 33.21% and 29.68% lower execution time, 26.36%, 29.64% and 29.67% lower map duration compared with the existing methods, such as ACO-APF-APP, APFA-APP, GWO-APP and PSO-APP.

Bathymetric simultaneous localisation and mapping (SLAM) methods yield accurate navigation results for autonomous underwater vehicles (AUVs) and can construct consistent seabed terrain maps. Multiple independently working vehicles can complete tasks like surveying and mapping efficiently, which means cooperative bathymetric SLAM using multiple AUVs is suitable for large-scale seabed mapping. However, the transmission of bathymetric measurements collected using a multi-beam echo sounder over a low bandwidth, noisy, and unreliable acoustic channel is difficult, making cooperative bathymetric SLAM very challenging. This paper develops a graph-based cooperative bathymetric SLAM system that can compress many bathymetric measurements into small-scale acoustic packets and yield accurate navigation results with a 10% loss of acoustic packets caused by unreliable acoustic communication. According to the simulation conducted using the field data, the new algorithm is shown to be robust and capable of providing accurate location and mapping results over a low bandwidth, noisy, and unreliable acoustic channel.

In recent years, the use of autonomous mobile robots has increased. SLAM (Simultaneous Localization and Mapping) technology is where an autonomous mobile robot estimates its own position and simultaneously maps the surrounding environment. It is widely used in robots that require autonomous movement. However, it is heavily burdened by computational tasks related to various types of image processing and calculations of physical environment recognition, which makes the system slow down due to limited hardware resources such as embedded systems. Various offload methods have been proposed, however they have not been applied to Robot Operating System (ROS) + SLAM applications and there are few examples of robot verification. To avoid the heavy load on the devices, we propose a method that takes the heavily load to the edge by using the prediction model based on the system resource information for a certain period related to SLAM processing. Based on preliminary experiences, we firstly develop a prediction model using system resource consumption data obtained from an actual SLAM execution and evaluate the prediction accuracy. The result shows that the accuracy varies as memory consumption increases, however the accuracy was high and useful in determining offload timing.

Remotely Operated Underwater Vehicles (ROUVs) are increasingly important for high-resolution surveying of narrow shipping lanes. This paper presents 3Clifs, a LiDAR- and AI-enhanced ROUV designed for near-real-time topographic mapping and navigation support in shallow, constrained waterways, demonstrated at three cliff sites in the Suez Canal. The system integrates three-dimensional Light Detection and Ranging (LiDAR) scanning, an Inertial Measurement Unit (IMU), and an onboard processor running the Robot Operating System (ROS) for Simultaneous Localization and Mapping (SLAM). To address data loss from underwater LiDAR (caused by scattering and reflection), we introduce an AI-driven optimisation module that reconstructs missing point cloud data and improves SLAM continuity. We also report propulsion and propeller design changes (propeller v05_1) that reduce flow turbulence and improve scan stability. We compare our approach to sonar-only ROUV mapping and to recent ROUV/LiDAR studies using metrics including point-cloud completeness, SLAM continuity, and navigation-path deviation. The main contributions are: (i) an integrated LiDAR, ROS and AI pipeline for underwater SLAM with missing-point recovery; (ii) a propulsion configuration optimized for LiDAR scanning stability; and (iii) a real-world Suez Canal case study demonstrating practical benefits for narrow-lane navigation.