Vision-aided inertial navigation system for robotic mobile mapping

Abstract

A mapping system by vision-aided inertial navigation was developed for areas where GNSS signals are unreachable. In this framework, a methodology on the integration of vision and inertial sensors is presented, analysed and tested. The system employs the method of ''SLAM: Simultaneous Localisation And Mapping'' where the only external input available to the system at the beginning of the mapping mission is a number of features with known coordinates. SLAM is a term used in the robotics community to describe the problem of mapping the environment and at the same time using this map to determine the location of the mapping device. Di¤er-ing from the robotics approach, the presented development stems from the frameworks of photogrammetry and kinematic geodesy that are merged in two filters that run in parallel: the Least-Squares Adjustment (LSA) for features coordinates determination and the Kalman filter (KF) for navigation correction. To test this approach, a mapping systemprototype comprising two CCD cameras and one Inertial Measurement Unit (IMU) is introduced. Conceptually, the outputs of the LSA photogrammetric resection are used as the external measurements for the KF that corrects the inertial navigation. The filtered position and orientation are subsequently employed in the photogrammetric intersection to map the surrounding features that are used as control points for the resection in the next epoch. We confirm empirically the dependency of navigation performance on the quality of the images and the number of tracked features, as well as on the geometry of the stereo-pair. Due to its autonomous nature, the SLAM's performance is further a¤ected by the quality of IMU initialisation and the a-priory assumptions on error distribution. Using the example of the presented system we show that centimetre accuracy can be achieved in both navigation and mapping when the image geometry is optimal.

Stereo-Image Matching Using a Speeded Up Robust Feature Algorithm in an Integrated Vision Navigation System

“…Later, Bayoud developed a mapping system using vision-aided inertial navigation (Bayoud and Skaloud, 2008). The system employs the method of Simultaneous Localization and Mapping (SLAM) where the only external inputs available to the system at the beginning of the mapping mission are a number of features with known coordinates.…”

Rigorous Integration of Inertial Navigation with Optical Sensors by Dynamic Networks

“…The first approach is a standard concept in mapping from airborne platforms [4,5], while the second is more useful in terrestrial mapping and/or robotics [6][7][8]. The term Simultaneous Localization and Mapping (SLAM) falls into the latter category [9].…”