Abstract
Abstract. 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.
Highlights
Small and micro Unmanned Aerial Vehicles (UAVs) has attracted great attention from researchers, casual users, and corporations because of the wide variety of applications that utilize the UAV to save time, cost, effort and their ability to execute dangerous tasks without exposing human lives to danger
The proposed approach performance and the time consumption is compared with the standard Iterative Closest Point (ICP)/Hector SLAM algorithm alone without the vehicle model (VM)-initialization step in two experiments that include harsh maneuver
Utilized for navigation regarding size, cost, and power consumption. These small UAVs navigation mainly depends on the fusion between the Global Navigation Satellite System (GNSS) and Inertial Measurement System (IMU), but during GNSS signal unavailability, the UAVs suffers from massive deterioration in the navigation states estimation, because of the rapidly accumulated errors of the low-cost MEMS-based IMU
Summary
Small and micro UAV has attracted great attention from researchers, casual users, and corporations because of the wide variety of applications that utilize the UAV to save time, cost, effort and their ability to execute dangerous tasks without exposing human lives to danger To perform these tasks, the UAV must be able to operate autonomously without human interventions. Other point-to–map and feature-to-feature matching algorithms are employed such as Hector SLAM (Kohlbrecher et al, 2013), or corner based scan matching The iterative fashion of these algorithms (ICP/Hector) increases time consumption, and it is not guaranteed to converge as it may fall in local minima, especially when there is harsh or fast movement or in the presence of moving objects.
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