Mapping the environment of a vehicle and localizing a vehicle within that unknown environment are complex issues. Although many approaches based on various types of sensory inputs and computational concepts have been successfully utilized for ground robot localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. This paper proposes a robust and efficient indoor mapping and localization solution for a UAV integrated with low-cost Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) sensors. Considering the advantage of the typical geometric structure of indoor environments, the planar position of UAVs can be efficiently calculated from a point-to-point scan matching algorithm using measurements from a horizontally scanning primary LiDAR. The altitude of the UAV with respect to the floor can be estimated accurately using a vertically scanning secondary LiDAR scanner, which is mounted orthogonally to the primary LiDAR. Furthermore, a Kalman filter is used to derive the 3D position by fusing primary and secondary LiDAR data. Additionally, this work presents a novel method for its application in the real-time classification of a pipeline in an indoor map by integrating the proposed navigation approach. Classification of the pipeline is based on the pipe radius estimation considering the region of interest (ROI) and the typical angle. The ROI is selected by finding the nearest neighbors of the selected seed point in the pipeline point cloud, and the typical angle is estimated with the directional histogram. Experimental results are provided to determine the feasibility of the proposed navigation system and its integration with real-time application in industrial plant engineering.

Today, enhancement in sensing technology enables the use of multiple sensors to track human motion/activity precisely. Tracking human motion has various applications, such as fitness training, healthcare, rehabilitation, human-computer interaction, virtual reality, and activity recognition. Therefore, the fusion of multiple sensors creates new opportunities to develop and improve an existing system. This paper proposes a pose-tracking system by fusing multiple three-dimensional (3D) light detection and ranging (lidar) and inertial measurement unit (IMU) sensors. The initial step estimates the human skeletal parameters proportional to the target user’s height by extracting the point cloud from lidars. Next, IMUs are used to capture the orientation of each skeleton segment and estimate the respective joint positions. In the final stage, the displacement drift in the position is corrected by fusing the data from both sensors in real time. The installation setup is relatively effortless, flexible for sensor locations, and delivers results comparable to the state-of-the-art pose-tracking system. We evaluated the proposed system regarding its accuracy in the user’s height estimation, full-body joint position estimation, and reconstruction of the 3D avatar. We used a publicly available dataset for the experimental evaluation wherever possible. The results reveal that the accuracy of height and the position estimation is well within an acceptable range of ±3–5 cm. The reconstruction of the motion based on the publicly available dataset and our data is precise and realistic.

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.

Over the years, gaze input modality has been an easy and demanding human–computer interaction (HCI) method for various applications. The research of gaze-based interactive applications has advanced considerably, as HCIs are no longer constrained to traditional input devices. In this paper, we propose a novel immersive eye-gaze-guided camera (called GazeGuide) that can seamlessly control the movements of a camera mounted on an unmanned aerial vehicle (UAV) from the eye-gaze of a remote user. The video stream captured by the camera is fed into a head-mounted display (HMD) with a binocular eye tracker. The user’s eye-gaze is the sole input modality to maneuver the camera. A user study was conducted considering the static and moving targets of interest in a three-dimensional (3D) space to evaluate the proposed framework. GazeGuide was compared with a state-of-the-art input modality remote controller. The qualitative and quantitative results showed that the proposed GazeGuide performed significantly better than the remote controller.

Augmented reality (AR) systems are becoming next-generation technologies to intelligently visualize the real world in 3D. This research proposes a sensor fusion based pipeline inspection and retrofitting for the AR system, which can be used in pipeline inspection and retrofitting processes in industrial plants. The proposed methodology utilizes a prebuilt 3D point cloud data of the environment, real-time Light Detection and Ranging (LiDAR) scan and image sequence from the camera. First, we estimate the current pose of the sensors platform by matching the LiDAR scan and the prebuilt point cloud data from the current pose prebuilt point cloud data augmented on to the camera image by utilizing the LiDAR and camera calibration parameters. Next, based on the user selection in the augmented view, geometric parameters of a pipe are estimated. In addition to pipe parameter estimation, retrofitting in the existing plant using augmented scene are illustrated. Finally, step-by-step procedure of the proposed method was experimentally verified at a water treatment plant. Result shows that the integration of AR with building information modelling (BIM) greatly benefits the post-occupancy evaluation process or pre-retrofitting and renovation process for identifying, evaluating, and updating the geometric specifications of a construction environment.