The fusion of inertial and visual data is widely used to improve an object's pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object's absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial / monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007(FP7/ -2013 under grant agreement n. 231855 (sFly). Gabriel Nützi is currently a Master student at the ETH
In REF , two methods, i.e. spline fitting and multi rate Extended Kalman Filter(EKF) are proposed to estimate the scale from an Inertial Measurement Unit (IMU).
Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM
Journal of Intelligent & Robotic Systems
Engineering Computer Science