Search

In this article, we present a comprehensive design and implementation for a micro aerial vehicle (MAV) that is able to perform 3-D autonomous navigation and obstacle avoidance in cluttered and realistic unknown environments without the aid of global positioning system and other external sensors or markers. To achieve these autonomous missions, modularized components are developed for the MAV, including visual–inertial odometry, 3-D occupancy mapping, and motion planning. The proposed system is implemented to run on a small embedded computer in real time. It is demonstrated to be robust in both simulation and real flight experiments. The demonstration video is available at: https://youtu.be/KUKzsnORm-4 .

This paper describes a novel method to improve the target location accuracy through imaging it from an aircraft. This method focuses on improving estimation accuracy of heading angle bias and then to improve geolocation performance. A particle swarm optimization algorithm is employed to derive an expression of optimal trajectory, which can be a guide for trajectory planning. Thanks to the maneuverability of quad-rotor unmanned aerial vehicles, the aircraft is commanded to follow path generated by trajectory planning to acquire multiple bearing measurements of the ground object. The main result is that the aircraft's heading angle bias can be more accurately estimated using trajectory planning. Hence, the target is more accurately geolocated. The efficacy of this technique is verified and demonstrated by simulation results and flight test.

In this paper, we present a 3D rotating laser-based navigation framework for micro aerial vehicles (MAVs) to fly autonomously in dynamic environments. It consists of a 6-degree of freedom (DoF) localization module and a 3D dynamic mapping module. A self-designed rotating laser scanner generates dense point clouds in which 3D features are extracted and aligned. The localization module is able to solve scan distortion issue while estimating the 6-DoF pose of MAVs. At the same time, the dynamic mapping module can further eliminate dynamic trails so that a clear dense 3D map is reconstructed. The dynamic targets are detected based on the spatial constraints and therefore without the need of dense point cloud clustering. Through filtering the detected dynamic obstacles, the localization approach can be robust to the dynamic environment variations. To verify the robustness and effectiveness of our proposed framework, we have tested our system in both real indoor environment with dynamic obstacles and outdoor foliage condition using a customized MAV platform.

This paper develops a method of calculating the three-dimensional (3-D) coordinates of a target with image information taken from an unmanned aerial vehicle (UAV). Unlike the usual approaches to target geolocation that rely heavily on georeferenced terrain database or accurate attitude sensors, the contribution of the proposed method is to offset the constraints and also perform 3-D geolocation of the target based on the relative altitude between the aircraft and the target calculated using the stereo vision technique. Considering the poor performance of the yaw-angle measurement provided by the low-quality attitude sensors in the UAV, a novel vision-based 3-D geolocation method is designed. The proposed method is proven to be valid and practical via simulation results and actual flight experiments.

In this work, we propose an infrared visual inertial odometry (VIO) algorithm for unmanned aerial vehicles (UAV) for both daytime and nighttime. It utilizes images from a single downward-looking thermal camera, and uses a laser scanner to measure relative distance to determine the scale factor. The scale recovery process consists of a comprehensive logic flow to try out measurements into a homography decomposition with a multi-plane constraint, obtaining the results with minimum error, making the algorithm more robust. Moreover, other modifications, such as keyframe idea and pruning KLT tracker with sub-regions further improves the performance. Besides, the results are fused with an on-board IMU through an EKF-based sensor fusion framework. This infrared visual inertial odometry algorithm can still navigate UAVs during the missions when GPS is down or not reliable or under poor illumination condition.

Driven by applications like Micro Aerial Vehicles (MAVs), driver-less cars, etc, localization solution has become an active research topic in the past decade. In recent years, Ultra Wideband (UWB) emerged as a promising technology because of its impressive performance in both indoor and outdoor positioning. But algorithms relying only on UWB sensor usually result in high latency and low bandwidth, which is undesirable in some situations such as controlling a MAV. To alleviate this problem, an Extended Kalman Filter (EKF) based algorithm is proposed to fuse the Inertial Measurement Unit (IMU) and UWB, which achieved 80Hz 3D localization with significantly improved accuracy and almost no delay. To verify the effectiveness and reliability of the proposed approach, a swarm of 6 MAVs is set up to perform a light show in an indoor exhibition hall. Video and source codes are available at https://github.com/lijx10/uwb-localization, Comment: ICCA 2018 (The 14th IEEE International Conference on Control and Automation)

This paper addresses the problem of autonomous flight in GPS-denied environments. We present our systematic solution with integrated hardware and software design, robust state estimation and mission management. A micro aerial vehicle (MAV) equipped with 2D laser scanner, camera, on-board computer and flight controller is designed and constructed to realize our flight missions. The 2D laser-based localization and Kalman filter-based sensor fusion provide a robust state estimation for the MAV. We also design a task tree-based mission management method to handle a complete mission with multiple tasks. Moreover, vision-based detection algorithms complement various tasks, such as target searching and precision landing. Extensive experimental results demonstrate the robustness and effectiveness of the overall integrated system. It was also validated by our championship winning in Singapore Amazing Flight Machine Competition (SAFMC) 2017.

This paper presents an implementation of a hybrid system consisting of a low-cost quadrotor and a small pushcart. The quadrotor is controlled with classical Proportional⿿Integral⿿Derivative (PID) controller for autonomous visual tracking and landing on the moving carrier. The vision-based tracking and landing approach utilizes enhancement of red, green and blue (RGB) color information rather than grayscale information of the helipad on the carrier, which shows fast and robust performance in different lighting conditions. This work is characteristic with utilizing the off-the-shelf affordable quadrotor and accomplishing the complex task using only the relative pixel position in image plane without communication between the quadrotor and carrier. The quadrotor's relative position to helipad is estimated with a frequency up to 30 Hz from the video stream, which enables the quadrotor to fly autonomously while performing real-time visual tracking and landing on the carrier. Series of experiments show that our system is easy to deploy and tune, simple and robust, also low-cost.

We present an approach for two-view motion segmentation for freely moving cameras, by formulating the epipolar constraint and spacial consistency into a discrete energy minimization problem, which can be efficiently solved using graph cut algorithms. With dense optical flow and proper sampling, a set of matched points is acquired for computing the fundamental matrix and the corresponding epipolar lines. The points distance to the epipolar lines, and their position on the image plane are used to construct a Markov Random Field (MRF) with discrete label-space. The 2-dimension label-space, i.e. motion area or static area, is computed using graph cut algorithms. We demonstrate the effectiveness of our method with the Johns Hopkins 155 motion dataset.

This paper presents a 3D on-line path planning algorithm for micro sized aerial vehicles (MAVs). The proposed approach adopts a two-layered planning framework. The first layer of the algorithm utilizes a sampling-based planner named Batch Informed Trees (BIT*) to quickly find an geometric obstacle-free passage. The second layer takes into account the dynamic constrains of the vehicle. By adopting a two-point boundary value problems (TPBVPs) approach, dynamically feasible trajectories can be generated efficiently within the previously found passage for lower-level controller. The main contribution of this work is proposing a complete on-line 3D path planning algorithm which can be implemented on the MAV with limited computational power.

Recent developments in robotics sensing using either the active sensor-laser range finder (LRF) or passive sensors-camera systems have shown that the existing approaches are able to estimate the motion and reconstruct the environment in a typical GPS-denied environment such as indoor environments. However, for a 2D LRF, it can only provide a planar measurement due to the hardware limitations and the cost for 3D LRF is still too high for robotic systems. For camera systems, the 3D perception capability and lightweight are promising while it is not effective in long range, low illumination and low textured environment com- pared to the LRF. In the proposed framework, our first contribution is a senor integration system that combines a stereo camera with a rotating LRF. The stereo camera can achieve a fast and smooth motion estimation and the rotating LRF could construct a dense 3D environment. Our second contribution is an integration of a fast feature-based motion estimation with an accurate shape matching refinement. The proposed approaches have been built in our customized unmanned aerial vehicle (UAV) platform. We have verified our proposed approach through a series of extensive evaluations in clustered indoor environments and open outdoor environments.

In this paper, we present a fully autonomous quadrotor for indoor exploration. The quadrotor is fully customized and capable of localization, mapping, planning and flying in unknown indoor environment with all real-time computations performed onboard. Two laser scanners are equipped to determine the 3D position of the quadrotor. The position measurements are further fused with Inertial Measurement Unit (IMU) to get a robust 6DOF state estimation. A depth camera is deployed to build 3D maps for the environment. Also, vision-based algorithms are designed to detect visual targets and perform precision landing. The whole system is verified in Singapore Amazing Flight Machine Competition (SAFMC). As Unmanned Systems Research Group from the National University of Singapore, we rank top in the fully autonomous category.

This paper presents a design and implementation of an unmanned aerial vehicle (UAV) for outdoor firefighting application. The proposed UAV firefighting system consists of a self-designed quadcopter as platform, a transmission system to collect and release water, a real time kinematic (RTK) based navigation system and a mission control system to monitor and coordinate the UAV. In our proposed autonomous cooperative framework, the UAV finds an optimal path (with respect to distance and power consumption) to the fire spot at first. After arriving at the target on fire, the mission control system will guide the UAV to suppress the fire. The proposed framework has been demonstrated in the 2015 International Micro Aerial Vehicle Outdoor Competition, held in Aachen, Germany and help our team V-Lion obtain the second place. Our designed framework has successfully achieved all the requirements in firefighting mission even in a challenging wind gusts environment. The video of the firefighting mission can be found at: https://youtu.be/YREngbun7zQ.