Comparative Study of Indoor Navigation Systems for Autonomous Flight

Recently, Unmanned Aerial Vehicles (UAVs) have attracted the society and researchers due to the capability to perform in economic, scientific and emergency scenarios, and are being employed in large number of applications especially during the hostile environments. They can operate autonomously for both indoor and outdoor applications mainly including search and rescue, manufacturing, forest fire tracking, remote sensing etc. For both environments, precise localization plays a critical role in order to achieve high performance flight and interacting with the surrounding objects. However, for indoor areas with degraded or denied Global Navigation Satellite System (GNSS) situation, it becomes challenging to control UAV autonomously especially where obstacles are unidentified. A large number of techniques by using various technologies are proposed to get rid of these limits. This paper provides a comparison of such existing solutions and technologies available for this purpose with their strengths and limitations. Further, a summary of current research status with unresolved issues and opportunities is provided that would provide research directions to the researchers of the similar interests

Accelerometers on Quadrotors : What do they Really Measure?

International audienceA revisited quadrotor model is proposed, including the so-called rotor drag. It differs from the model usually considered, even at first order, and much better explains the role of accelerometer feedback in control algorithms. The theoretical derivation is supported by experimental data

State estimation for aggressive flight in GPS-denied environments using onboard sensing

In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Semi-dense SLAM on an FPGA SoC

Deploying advanced Simultaneous Localisation and Mapping, or SLAM, algorithms in autonomous low-power robotics will enable emerging new applications which require an accurate and information rich reconstruction of the environment. This has not been achieved so far because accuracy and dense 3D reconstruction come with a high computational complexity. This paper discusses custom hardware design on a novel platform for embedded SLAM, an FPGA-SoC, combining an embedded CPU and programmable logic on the same chip. The use of programmable logic, tightly integrated with an efficient multicore embedded CPU stands to provide an effective solution to this problem. In this work an average framerate of more than 4 frames/second for a resolution of 320×240 has been achieved with an estimated power of less than 1 Watt for the custom hardware. In comparison to the software-only version, running on a dual-core ARM processor, an acceleration of 2× has been achieved for LSD-SLAM, without any compromise in the quality of the result

Indoor Navigation with a Swarm of Flying Robots

Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or long-range communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments

ENHANCEMENT OF REAL-TIME SCAN MATCHING FOR UAV INDOOR NAVIGATION USING VEHICLE MODEL

Autonomous Unmanned Aerial Vehicles (UAVs) have drawn great attention from different organizations, because of the various applications that save time, cost, effort, and human lives. The navigation of autonomous UAV mainly depends on the fusion between Global Navigation Satellite System (GNSS) and Inertial Measurement System (IMU). Navigation in indoor environments is a challenging task, because of the GNSS signal unavailability, especially when the utilized IMU is low-cost. Light Detection and Ranging Radar (LIDAR) is one of the mainly utilized sensors in the indoor environment for localization through scan matching of successive scans. The process of calculating the rotation and translation from successive scans can employ different approaches, such as Iterative Closest Point (ICP) with its variants, and Hector SLAM. ICP and Hector SLAM iterative fashion can greatly increase the matching time, and the convergence is not guaranteed in case of harsh maneuvers, moving objects, and short-range LIDAR as it may get stuck in local minima. This paper proposes enhanced real-time ICP and Hector SLAM algorithms based on vehicle model (VM) during sharp maneuvers. The vehicle model serves as initialization step (coarse alignment) then the ICP/Hector serve as fine alignment step. Test cases of quadcopter flight with harsh maneuvers were carried out with LIDAR to evaluate the proposed approach to enhance the ICP/Hector convergence time and accuracy. The proposed algorithm is convenient for UAVs where there are limitations regarding the size, weight, and power limitations, as it is a stand-alone algorithm that does not require any additional sensors