Applicability of New Approaches of Sensor Orientation to Micro Aerial Vehicles

Abstract

ABSTRACT:This study highlights the benefits of precise aerial position and attitude control in the context of mapping with Micro Aerial Vehicles (MAVs). Accurate mapping with MAVs is gaining importance in applications such as corridor mapping, road and pipeline inspections or mapping of large areas with homogeneous surface structure, e.g. forests or agricultural fields. There, accurate aerial control plays a major role in successful terrain reconstruction and artifact-free ortophoto generation. The presented experiments focus on new approaches of aerial control. We confirm practically that the relative aerial position and attitude control can improve accuracy in difficult mapping scenarios. Indeed, the relative orientation method represents an attractive alternative in the context of MAVs for two reasons. First, the procedure is somewhat simplified, e.g. the angular misalignment, so called boresight, between the camera and the inertial measurement unit (IMU) does not have to be determined and, second, the effect of possible systematic errors in satellite positioning (e.g. due to multipath and/or incorrect recovery of differential carrier-phase ambiguities) is mitigated. First, we present a typical mapping project over an agricultural field and second, we perform a corridor road mapping. We evaluate the proposed methods in scenarios with and without automated image observations. We investigate a recently proposed concept where adjustment is performed using image observations limited to ground control and check points, so called fast aerial triangulation (Fast AT). In this context we show that accurate aerial control (absolute or relative) together with a few image observations can deliver accurate results comparable to classical aerial triangulation with thousands of image measurements. This procedure in turns reduces the demands on processing time and the requirements on the existence of surface texture. Finally, we compare the above mentioned procedures with direct sensor orientation (DiSO) to show its potential for rapid mapping.

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

“…The option of using RGB camera external parameters computed by the SfM photogrammetric processing has been considered to estimate UAV trajectory [30]. The hyperspectral acquisition is performed at 50 Hz and taking 10 photos per second would enable camera position and attitude to be computed at 10 Hz.…”

A Collaborative Framework for High-Definition Mapping

“…To achieve this, feature points are extracted and matched within massive amounts of images, additional sensing outputs are added if available, and a global optimization (bundle adjustment) is operated [7]. Geolocalized data can be included through the use of GNSS sensors [8], making the accuracy sensitive to GNSS flaws, and by manually installing geo-localized anchor points in the scene [9], which is a tedious and expensive operation. This process must be performed offline, and is unscalable for producing large maps of accurate landmarks.…”

The Evaluation of Photogrammetry-Based DSM from Low-Cost UAV by LiDAR-Based DSM

“…The GCPs' positions (x, y, z coordinates) were measured using the Trimble GNSS receiver connected with the Croatian Positioning System (CROPOS) which enables to obtain both horizontal and vertical positional accuracy from 2 to 5 cm (CROPOS -Users' Manual). Due to dense forest and mostly invisible ground from the air, it was not possible to provide (set up) the regular spatial distribution of GCPs over the entire study area which enables the most accurate orientation of images [26]. Therefore, GCPs were set up and measured on the forest roads from where they can be easily detected on UAV images (Figure 1).…”