Bundle adjustment with raw inertial observations

t is well known that accurate aerial position and attitude control is beneficial for image orientation in airborne photogrammetry. The aerial control is traditionally obtained by Kalman filtering/smoothing inertial and GNSS observations prior to the bundle-adjustment. However, in Micro Aerial Vehicles this process may result in poor attitude determination due to the limited quality of the inertial sensors, large alignment uncertainty and residual correlations between sensor biases and initial attitude. We propose to include the raw inertial observations directly into the bundle-adjustment instead of as position and attitude weighted observations from a separate inertial/GNSS fusion step. The necessary observation models are derived in detail within the context of the so called “Dynamic Networks”. We examine different real world cases and we show that the proposed approach is superior to the established processing pipeline in challenging scenarios such as mapping in corridors and in areas where the reception of GNSS signals is denied.

Applicability Of New Approaches Of Sensor Orientation To Micro Aerial Vehicles

Martin Rehak, Jan Skaloud

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.

Copernicus Gesellschaft Mbh2016

Autonomous and non-autonomous dynamic model based navigation system for unmanned vehicles

Mehran Khaghani, Jan Skaloud

A navigation system including a vehicle dynamic model (VDM) that serves as the main process model within a navigation filter is described. When used in an unmanned aerial vehicle (UAV), the navigation system may work in communication with inertial measurement units (IMUs) and environment dependent sensors such as GNSS receivers. Particularly, the navigation system is beneficial in the case of GNSS signal reception outages, where conventional IMU coasting drifts quickly. Yet, the navigation system may also be employed in other scenarios, for example during GNSS presence for improved positioning, velocity and attitude determination, or in combination with GNSS when no IMU is available by design or due to a failure. In the navigation system, a solution to VDM equations provides an estimate of position, velocity, and attitude, which can be updated within a navigation filter based on available observations, such as IMU data or GNSS measurements.

2016

Applicability Of New Approaches Of Sensor Orientation To Micro Aerial Vehicles

Martin Rehak, Jan Skaloud

Copernicus Gesellschaft Mbh2016