Simultaneous localization and mapping (SLAM) represents a crucial algorithm in the autonomous navigation of ground vehicles. Several studies were conducted to improve the SLAM algorithm using various sensors and robot platforms. However, only a few works have focused on applications inside low-illuminated featureless tunnel environments. In this work, we present an improved SLAM algorithm using wheel encoder data from an autonomous ground vehicle (AGV) to obtain robust performance in a featureless tunnel environment. The improved SLAM system uses FAST-LIO2 LiDAR SLAM as the baseline algorithm, and the additional wheel encoder sensor data are integrated into the baseline SLAM structure using the extended Kalman filter (EKF) algorithm. The EKF algorithm is used after the LiDAR odometry estimation and before the mapping process of FAST-LIO2. The prediction step uses the wheel encoder and inertial measurement unit (IMU) data, while the correction step uses the FAST-LIO2 LiDAR state estimation. We used an AGV to conduct experiments in flat and inclined terrain sections in a tunnel environment. The results showed that the mapping and the localization process in the SLAM algorithm was greatly improved in a featureless tunnel environment considering both inclined and flat terrains.

This paper proposes a method of point cloud generation for indoor low-visibility disaster environments. Recently, robots have been developed to perform several missions in such environments, which are potentially harmful to humans. However, an indoor disaster environment often consists of a dense fog, which makes robot navigation challenging because widely used sensors (e.g., optical cameras and light detection and ranging (LiDAR)) cannot be used due to low-visibility. Several methods have been used to address this problem. In this paper, we propose a sensor-fusion method that can generate point clouds of uneven foggy indoor environments using LiDAR and stereo thermal infrared cameras. We generate point clouds using stereo depth estimation and process them to have the same angular resolution as a LiDAR point cloud. We then approximate them based on thermal edge information and finally integrate the LiDAR point cloud with fog points removed. Furthermore, we performed an indoor experiment and the results showed that the proposed method can generate applicable point clouds by applying conventional LiDAR odometry and mapping algorithms.

This paper addresses a novel calibration based estimation method to measure the forward seabed elevation (FSE) where an area illuminated by forward scan sonar (FSS) for unmanned underwater vehicles (UUVs).The conventional method measures the FSE using one or two down-facing single-beam sonars; this method can, however, cause errors. In order to accurately measure the FSE, a sensor capable of measuring the two-dimensional space of the seabed is needed, such as FSS. The method proposed in this work consists in estimating the FSE by analyzing the acoustic beam distribution characteristics of FSS. A novel FSS beam distribution model is proposed, which simplifies beam distribution characteristics. Then, on-site calibration is conducted to estimate the parameters of this model. Based on the estimated model, templates can be generated and compared with FSS data acquired at the experiment in order to estimate geometrical information at the FSE. The FSE of the best matched template is the estimated FSE of the FSS data acquired in the experiment. In order to verify the performance of the proposed method, experiments using an UUV were carried out in an indoor water tank. We also verified that the proposed method performed well even when there were objects on the seabed.

This study details the development of a semi-active suspension wheel module for small mobile robots and assesses its dynamic characteristics under various driving conditions through simulation. The wheel module features a low-degree-of-freedom mechanical design and includes a semi-active damper to improve adaptability to different environments. To validate the simulation model, a prototype robot equipped with the wheel module was created, and obstacle-crossing experiments were conducted to measure vertical acceleration responses. The model was then refined based on these experimental results. By employing design of experiments and optimization techniques, the effective range of damping coefficients was estimated. Additionally, simulations were carried out at different speeds, payloads, and obstacle heights to identify optimal damping values and examine their trends. The results indicate that the proposed module significantly enhances driving stability and can serve as a foundation for future control strategies in robotic mobility systems.