Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments.
Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation
Department of Electrical and
Michigan Technological University,
Houghton, MI 49931
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received July 12, 2016; final manuscript received November 29, 2016; published online April 18, 2017. Assoc. Editor: Davide Spinello.
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Deilamsalehy, H., Havens, T. C., and Manela, J. (April 18, 2017). "Heterogeneous Multisensor Fusion for Mobile Platform Three-Dimensional Pose Estimation." ASME. J. Dyn. Sys., Meas., Control. July 2017; 139(7): 071002. https://doi.org/10.1115/1.4035452
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