The use of autonomous surface vehicles (ASVs) is an efficient alternative to the traditional manual or static sensor network sampling for large-scale monitoring of marine and aquatic environments. However, navigating natural and narrow waterways is challenging for low-cost ASVs due to possible obstacles and limited precision global positioning system (GPS) data. Visual information coming from a camera can be used for collision avoidance, and digital image stabilization is a fundamental step for achieving this capability. This work presents an implementation of an image stabilization algorithm for a heterogeneous low-power board (i.e., NVIDIA Jetson TX1). In particular, the paper shows how such an embedded vision application has been configured to best exploit the CPU and the GPU processing elements of the board in order to obtain both computation performance and energy efficiency. We present qualitative and quantitative experiments carried out on two different environments for embedded vision software development (i.e., OpenCV and OpenVX), using real data to find a suitable solution and to demonstrate its effectiveness. The data used in this study is publicly available.
Fast and Power-efficient Embedded Software Implementation of Digital Image Stabilization for Low-cost Autonomous Boats
Bloisi Domenico Daniele;
2017-01-01
Abstract
The use of autonomous surface vehicles (ASVs) is an efficient alternative to the traditional manual or static sensor network sampling for large-scale monitoring of marine and aquatic environments. However, navigating natural and narrow waterways is challenging for low-cost ASVs due to possible obstacles and limited precision global positioning system (GPS) data. Visual information coming from a camera can be used for collision avoidance, and digital image stabilization is a fundamental step for achieving this capability. This work presents an implementation of an image stabilization algorithm for a heterogeneous low-power board (i.e., NVIDIA Jetson TX1). In particular, the paper shows how such an embedded vision application has been configured to best exploit the CPU and the GPU processing elements of the board in order to obtain both computation performance and energy efficiency. We present qualitative and quantitative experiments carried out on two different environments for embedded vision software development (i.e., OpenCV and OpenVX), using real data to find a suitable solution and to demonstrate its effectiveness. The data used in this study is publicly available.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
