| Texto completo | |
| Autor(es): |
Caldas, Kenny A. Q.
;
Inoue, Roberto S.
;
Terra, Marco H.
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 IEEE SENSORS; v. N/A, p. 4-pg., 2022-01-01. |
| Resumo | |
We propose in this work a non-parametric Gaussian Process Regression (GPR) model to estimate reduced drift velocity of a commercial Micro Air Vehicle (MAV) by using a single additional Inertial Measurement Unit and odometry data, without relying on expensive or processing demanding sensors. The main advantage of this approach is that it does not require sensor calibration and parameter tuning for accurate velocity estimation. We considered three different kernels in the GPR training and evaluated the proposed method on two datasets. The results are compared with the MAV odometry and a Visual Simultaneous Localization and Mapping (SLAM) algorithm against ground truth data obtained from a high precision motion tracking system. Our proposed method demonstrates superior performance in both datasets and provides a viable solution to reduce position drift on commercial MAVs. (AU) | |
| Processo FAPESP: | 14/50851-0 - INCT 2014: Instituto Nacional de Ciência e Tecnologia para Sistemas Autônomos Cooperativos Aplicados em Segurança e Meio Ambiente |
| Beneficiário: | Marco Henrique Terra |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 18/13848-1 - Planejamento de trajetória de robôs autônomos heterogêneos para o mapeamento em 3D cooperativo de um ambiente desconhecido |
| Beneficiário: | Kenny Anderson Queiroz Caldas |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |