Unmanned Aerial Vehicle Indoor Research Articles

A Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms

The potential of indoor unmanned aerial vehicle (UAV) localization is paramount for diversified applications within large industrial sites, such as hangars, malls, warehouses, production lines, etc. In such real-time applications, autonomous UAV location is required constantly. This paper comprehensively reviews radio signal-based wireless technologies, machine learning (ML) algorithms and ranging techniques that are used for UAV indoor positioning systems. UAV indoor localization typically relies on vision-based techniques coupled with inertial sensing in indoor Global Positioning System (GPS)-denied situations, such as visual odometry or simultaneous localization and mapping employing 2D/3D cameras or laser rangefinders. This work critically reviews the research and systems related to mini-UAV localization in indoor environments. It also provides a guide and technical comparison perspective of different technologies, presenting their main advantages and disadvantages. Finally, it discusses various open issues and highlights future directions for UAV indoor localization.

An Improved Probabilistic Roadmap Planning Method for Safe Indoor Flights of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been widely used in industry and daily life, where safety is the primary consideration, resulting in their use in open outdoor environments, which are wider than complex indoor environments. However, the demand is growing for deploying UAVs indoors for specific tasks such as inspection, supervision, transportation, and management. To broaden indoor applications while ensuring safety, the quadrotor is notable for its motion flexibility, particularly in the vertical direction. In this study, we developed an improved probabilistic roadmap (PRM) planning method for safe indoor flights based on the assumption of a quadrotor model UAV. First, to represent and model a 3D environment, we generated a reduced-dimensional map using a point cloud projection method. Second, to deploy UAV indoor missions and ensure safety, we improved the PRM planning method and obtained a collision-free flight path for the UAV. Lastly, to optimize the overall mission, we performed postprocessing optimization on the path, avoiding redundant flights. We conducted experiments to validate the effectiveness and efficiency of the proposed method on both desktop and onboard PC, in terms of path-finding success rate, planning time, and path length. The results showed that our method ensures safe indoor UAV flights while significantly improving computational efficiency.

System Engineering-Based Conceptual Design of Indoor Navigation System of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been become important day by day because of advantages in our life use cases; specially, they can play important role in the industry 4.0 concept such as automation in production plants. System engineering approach is planning the design and production phases. Every system, subsystem, component, or mission of the systems are investigated before project starts and each requirement is set. The investigation of compatibility between systems and products minimizes the risks during the production phases. This paper proposes the usage of system engineering approach to the conceptual design of an UAV navigation system which aims to be used in indoor environments. In order to provide autonomous indoor flight, system requirements are set by using system engineering approach. Then step by step, the basic functions that UAV needs to complete operation are specified. The required products are listed as a tree and the relation between products and functions are investigated. Thus, the conceptual design of the UAV indoor navigation system is demonstrated by using of the functional tree, the functions and product relation matrix, the product tree, and the use cases. Moreover, the system operational requirements are investigated, and the requirement operation phases, modes and use cases are also determined. Thanks to system engineering, all these complex systems are put together systematically, and the conceptual design is achieved.

GNSS-denied UAV indoor navigation with UWB incorporated visual inertial odometry

The localization of unmanned aerial vehicles (UAVs) in GPS-denied areas is an essential issue for indoor navigation. This paper presents a technique to improve the accuracy of visual inertial odometry (VIO) by combining the ultra-wideband (UWB) positioning technology. The proposed architecture is divided into two stages. In the initial stage, the constraint on UWB short-term position change is adopted to improve the pose estimation results of the VIO system. It is also used to mitigate the translation error caused by the vibration and lack of features during the flight. In the second stage, a loose coupling approach based on nonlinear optimization is utilized to fuse the local pose estimator of the VIO system with the global constraints from the UWB positioning. At the beginning of each operation, the alignment between the VIO and UWB frames is estimated to avoid the influence of coordinate transformation due to the VIO cumulative error. It is shown that our optimization-based fusion method is able to achieve a smooth localization trajectory under the global coordinate frame. In the experiments, the performance evaluation carried out in the real-world scenes has demonstrated the effectiveness of the proposed technique.

Brain computer interface system based on monocular vision and motor imagery for UAV indoor space target searching

This paper puts forward a brain computer interface (BCI) system that integrates the monocular vision navigation and decision subsystems to realize the indoor three-dimensional (3D) space target searching with a low speed Unmanned Aerial Vehicle (UAV). The monocular vision navigation subsystem provides 3D space feasible flight directions for the decision subsystem. It utilizes the Scale Invariant Feature Transform (SIFT) algorithm and Brute-Force (BF) algorithm to extract the key points and match the extracted feature points. Obstacles and their positions are estimated by comparing the changes in the size of key points and the ratio of the size of the “convex points” area of the interest targets. The decision subsystem collects four kinds of motor imaginary (MI) tasks (left/right-hand, feet and tongue) electroencephalogram (EEG) signals from 15 electrodes firstly. Then, it applies two fifth-order Butterworth Band-Pass Filter (BPF) to preprocess the raw MI tasks EEG signals. Next, it adopts the Common Spatial Pattern (CSP) to spatially filter the preprocess EEG signals. To implement the quaternary classification,the decision subsystem utilizes the single convolutional layer Convolutional Neural Network (CNN) to realize the MI tasks EEG signals feature extraction, classification and final feasible flight direction decision finally. During this process, the spectrograms of the spatially filtered signals are given as the input to CNN. The actual indoor 3D space target searching experiment verifies that this BCI system has good adaptability and control stability.

Grid-optimized UAV indoor path planning algorithms in a complex environment

Path planning has become a predominant issue for unmanned aerial vehicles (UAVs), especially in complex indoor environments. Existing solutions for UAV indoor path planning are strongly limited due to their high computational complexity, slow convergence speed and poor flight paths. In this study, a set of grid-optimized UAV indoor path planning algorithms targeting complex environments is proposed to solve the abovementioned issues. First, the grid-optimized indoor airspace modeling (GO-IAM) algorithm based on the Geo-graphical coordinates Subdividing grid with One-dimension integral coding on 2n-Tree in 3 Dimensions (GeoSOT-3D) is proposed to reduce the computational complexity of modeling the indoor 3D airspace by means of spatial subdivisions. For regular indoor scenarios and a complex “dead zone” airspace, the grid-optimized A* path planning (GO-APP) algorithm and the grid-optimized local backtracking path planning (GO-LBPP) algorithm are established to address the efficiency and flyability of path planning. Comparison experiments of multiple UAV indoor path planning algorithms reveal that the GO-APP algorithm has the shortest computation time and planning path. Specifically, with the use of the GO-LBPP algorithm, the indoor local dead zone issue can be solved, and the deadlock of the planning path can be avoided, which cannot be achieved by other algorithms. Hence, the proposed grid-optimized UAV indoor path planning algorithms can realize state-of-the-art path planning through spatial subdivision in complex 3D indoor airspace.

Tightly coupled integrated navigation system via factor graph for UAV indoor localization

Due to the widely use of the rotorcrafts in civil applications, the highly accurate positioning is paid more attentions. The ultra-wide band (UWB) receiver plays a crucial role in navigating the unmanned aerial vehicle (UAV) in indoor areas, because of its low cost and low power consumption. However, the positioning accuracy of UWB is drastically affected by the infamous multipath effect. Therefore, the Ultra Wideband (UWB)/Inertial Navigation System (INS) integrated indoor navigation is an effective approach to reduces the positioning errors. This paper proposes a tightly-coupled UWB/INS integration navigation based on factor graph optimization (FGO). For the loosely-coupled integration, the linear and nonlinear least square methods are employed to obtain the well-performed single point positioning. The Allan-variance analysis is used to estimate the process noise covariance of INS. Besides, to reduce the computational load of nonlinear optimization in factor graph, this paper employs the IMU preintegration factor. The location performance of the proposed FGO method is compared with an extended Kalman Filter (EKF). The results show that the proposed tightly-coupled UWB/INS integration method can realize the better positioning performance than that of the conventional EKF in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) combined indoor environments.

A New Velocity Meter Based on Hall Effect Sensors for UAV Indoor Navigation

This paper presents a new approach to enhance the indoor navigation of unmanned aerial vehicles (UAVs). During indoor flight missions, UAVs rely on the integration of inertial navigation systems (INS) and aiding sensors measurements to estimate the UAV’s navigation states. These aiding measurements may be position, velocity, or attitudes. For indoor environments, the presence of absolute positioning systems, such as Wi-Fi, as an aiding sensor is not always guaranteed. Therefore, alternative aiding sources are required to mitigate the drift exhibited in INS and enhance the navigation accuracy. Different sensors have been utilized to aid INS. These aiding sensors should be carefully chosen, as they should not exceed the cost, weight, size, and power consumption limitations of the UAV. This paper presents a new utilization of Hall effect sensors to act as a flying air odometer (Air-Odo) for quadcopter UAV and to aid the INS with velocity update. Testing results confirmed the capabilities of the new approach to aid the navigation solution through real experiments, with velocity update applied to an extended Kalman filter. The new approach enhanced the navigation solution by 98% when compared with the stand-alone INS solution.

A novel distributed architecture for UAV indoor navigation

Abstract In the last decade, different indoor flight navigation systems for small Unmanned Aerial Vehicles (UAVs) have been investigated, with a special focus on different configurations and on sensor technologies. The main idea of this paper is to propose a distributed Guidance Navigation and Control (GNC) system architecture, based on Robotic Operation System (ROS) for light weight UAV autonomous indoor flight. The proposed framework is shown to be more robust and flexible than common configurations. A flight controller and companion computer running ROS for control and navigation are also included in the section. Both hardware and software diagrams are given to show the complete architecture. Further works will be based on the experimental validation of the proposed configuration by indoor flight tests.

A Comprehensive UAV Indoor Navigation System Based on Vision Optical Flow and Laser FastSLAM

This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle (UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or off-line computational power, while the mapping is done off-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.

A Comprehensive UAV Indoor Navigation System Based on Vision Optical Flow and Laser FastSLAM

This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle(UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or of-line computational power, while the mapping is done of-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.