ICUAS'17 Paper Abstract


Paper WeA5.5

JANG, DONGJIN (Hanseo Univ.), Park, Jinyong (Hanseo University), Lee, Dongjin (Hanseo University)

Accelerated Point Mass Filter for Vision-Aided Terrain Referenced Navigation

Scheduled for presentation during the "UAS Navigation - I" (WeA5), Wednesday, June 14, 2017, 11:20−11:40, San Marco Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 12, 2021

Keywords Navigation, Sensor Fusion, UAS Applications


In this paper, a vision-aided terrain referenced navigation (VATRN) algorithm constructed by point-mass filter (PMF) is accelerated by graphic processing unit (GPU). The terrain referenced navigation algorithm estimates the vehicles position by blending INS data with measured terrain height, and matching that data with the stored digital terrain elevation database (DTED). On the other hands, the VATRN algorithm obtains odometry data from visual sensors instead of inertial sensors. The odometry data is estimated by the homography relationship of two successive ground images of a monocular camera. Point-mass filter is one of the TRN algorithm based on the Bayesian estimation theory, and it contains convolutional integral of each points for the time update process. The convolution is the computational burden and can be accelerated by parallel computing to improve the estimation performance of PMF with sufficient point grids. GPU is employed to accelerate the PMF and numerical simulations are performed to analyze and evaluate the performance of the proposed method. The results show that the precise autonomous navigation of unmanned aircraft is achieved by the accelerated vision-based TRN algorithm.



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