Bundle adjustment | EPFL Graph Search

In photogrammetry and computer stereo vision, bundle adjustment is simultaneous refining of the 3D coordinates describing the scene geometry, the parameters of the relative motion, and the optical characteristics of the camera(s) employed to acquire the images, given a set of images depicting a number of 3D points from different viewpoints. Its name refers to the geometrical bundles of light rays originating from each 3D feature and converging on each camera's optical center, which are adjusted optimally according to an optimality criterion involving the corresponding image projections of all points. Uses Bundle adjustment is almost always used as the last step of feature-based 3D reconstruction algorithms. It amounts to an optimization problem on the 3D structure and viewing parameters (i.e., camera pose and possibly intrinsic calibration and radial distortion), to obtain a reconstruction which is optimal under certain assumptions regarding the noise pertaining to the obs

On the adjustment, calibration and orientation of drone photogrammetry and laser-scanning

Emmanuel Pierre Quentin Clédat

Centimetre level precision mapping is essential for many applications such as land-use, infrastructure inspection, cultural heritage preservation, and construction site monitoring. However, the acquisition and its preparation (in particular the setting of a ground control point network (GCPs)) are still expensive or even impossible in cluttered or dangerous areas. The recent development of UAVs together with the miniaturization of the sensors is a promising evolution for reducing costs and expand opportunities. The sensors embedded on the drone: GNSS antenna, IMU, camera and (optional) LIDAR are light and often low-cost. The low quality of their raw measurements must be counterbalanced by their rigorous modeling in order to obtain accurate final results: if we cannot expect the sensors to be error-free, one must model these in order to correct them. This is achieved by in-situ calibration or on a dedicated calibration field, together with a rigorous fusion of the raw data acquired by the different sensors with the so-called bundle-adjustment method. This thesis proposes several models to describe the behavior of the sensors, in order to hybridize them rigorously in the bundle-adjustment. Consistent datasets have been acquired on the field specifically to assess the relevance of both the sensor models and their hybridizing in complex photogrammetric processing. The contribution of this thesis could be divided into two mains categories. On one hand, this thesis suggests tools and recommendation to improve directly the procedures achieved by end-users using current UAV-mapping commercial solutions (in particular for the GCPs placement, for the choice of the camera calibration and model and for the flight-plan). On the other hand, this thesis put forward exotic methods (methods considered as exotic at the time of the writing of the thesis) such as Photo-LIDAR hybridizing and collaborative mapping achieved by a terrestrial-aerial tandem (a terrestrial vehicle holding a LIDAR, GNSS, imaging and inertial sensors followed by a drone conceived to proceed to airborne photogrammetry) or an aerial-aerial tandem (two drones flying in formation to proceed to airborne photogrammetry). The contribution of this thesis will permit to reduce costs, to improve the quality of mapping products and to enlarge the possibilities of mapping: in particular, map cluttered or inaccessible zones which are nowadays considered as difficult or even impossible to map.

EPFL2020

Fusion of Photo with Airborne Laser Scanning

Emmanuel Pierre Quentin Clédat, Jan Skaloud

Photogrammetry and Laser-Scanning are usually considered as complementary. Integration of these two observation methods has the potential to blend their individual advantages. The resulting benefit is likely to be higher in drone airborne mapping, which payload capacity (and thus the quality of the embedded IMU) is limited. Thus, the trajectory computed by the IMU is subject to important time-dependent errors: even if the global attitude is less adequate, it is self-coherent locally. For this reason, we propose a close integration of Photogrammetry with Laser-Scanning based on the correction of time-dependent error of the trajectory with the help of the image observations acquired by the camera. Apart from the trajectory, this hybridization requires optical correspondences between image and Laser measurements. Such full set of input data is rigorously fused together in a Bundle-Adjustment in order to better determine the trajectory, and thus the resulting point-cloud. The presented theory was practically evaluated in an airborne case against a reference solution.

2020

Bundle adjustment with raw inertial observations

Davide Antonio Cucci, Martin Rehak, Jan Skaloud

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.

2017

On the adjustment, calibration and orientation of drone photogrammetry and laser-scanning

Emmanuel Pierre Quentin Clédat

EPFL2020