Collision-Free Navigation of Small UAVs in Complex Urban Environment

Small unmanned aerial vehicles (UAVs) are expected to become highly innovative solutions for all kind of tasks such as transport, surveillance, inspection or guidance, and many commercial ideas already exist. Herein, small multi rotor UAVs are preferred since they are easy to construct and to fly, at least in wide open spaces. However, many UAV business cases are foreseen in complex urban environments which are very challenging from the perspective of UAV flight. Our work focuses on the autonomous flight and collision-free navigation in an urban environment, where GPS is still considered for localization but where variations in the accuracy or temporary unavailability of GPS position data is explicitly considered. Herein, urban environments are challenging because they require flight nearby large structures and also nearby moving obstacles such as humans and other moving objects, at low altitudes or in very narrow spaces and thus also in areas where GPS (global positioning system) position data might temporarily be very inaccurate or even not available. Therefore we designed a custom stereo camera with adjustable base length for the perception of the possible potential obstacles in the unknown outdoor environment. In this context the optimal design and sensitivity parameters are investigated in outdoor experiments. Using the stereo images, graph based SLAM approach is used for online three dimensional mapping of the static and dynamic environment. For the memory efficiency incremental online loop closure detection using bag of words method is implemented here. By having the three dimensional map, the cost of the cell and its transition calculated in real time by the modified D* lite which will search and generate three dimensional collision free path planning. Experiments of the 3D mapping and collision free path planning are conducted using small UAV in outdoor scenario. The combined experimental results of real time mapping and path planning demonstrated that the three dimensional collision free path planning is able to handle the real time computational constraints while maintaining safety distance

Real-time graph-based SLAM in unknown environments using a small UAV

Autonomous navigation of small Unmanned Aerial Vehicles (UAVs) in cluttered environments is still a challenging problem. In this work, we present an approach based on graph slam and loop closure detection for online mapping of unknown outdoor environments using a small UAV. Here, we used an onboard front facing stereo camera as the primary sensor. The data extracted by the cameras are used by the graph-based slam algorithm to estimate the position and create the graph-nodes and construct the map. To avoid multiple detections of one object as different objects and to identify re-visited locations, a loop closure detection is applied with optimization algorithm using the g2o toolbox to minimize the error. Furthermore, 3D occupancy map is used to represent the environment. This technique is used to save memory and computational time for the online processing. Real experiments are conducted in outdoor cluttered and open field environments.The experiment results show that our presented approach works under real time constraints, with an average time to process the nodes of the 3D map is 17.79ms

Towards an autonomous vision-based unmanned aerial system against wildlife poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing

Visual odometry based absolute target geo-location from micro aerial vehicle

An unmanned aerial system capable of finding world coordinates of a ground target is proposed here. The main focus here was to provide effective methodology to estimate ground target world coordinates using aerial images captured by the custom made micro aerial vehicle (MAV) as a part of visual odometery process on real time. The method proposed here for finding target's ground coordinates uses a monocular camera which is placed in MAV belly in forward looking/ Downward looking mode. The Binary Robust Invariant Scalable Key points (BRISK) algorithm was implemented for detecting feature points in the consecutive images. After robust feature point detection, efficiently performing Image Registration between the aerial images captured by MAV and with the Geo referenced images is the prime and core computing operation considered. Precise Image alignment is implemented by accurately estimating Homography matrix. In order to accurately estimate Homography matrix which consists of 9 parameters, this algorithm solves the problem in a Least Square Optimization way. Therefore, this framework can be integrated with visual odometery pipeline; this gives the advantage of reducing the computational burden on the hardware. The system can still perform the task of target geo-localization efficiently based on visual features and geo referenced reference images of the scene which makes this solution to be found as cost effective, easily implementable with robustness in the output. The hardware implementation of MAV along with this dedicated system which can do the proposed work to find the target coordinates is completed. The main application of this work is in search and rescue operations in real time scenario. The methodology was analyzed and executed in MATLAB before implementing real time on the developed platform. Finally, three case studies with different advantages derived from the proposed framework are represented