A general approach to time-varying parameters in pose-graph optimization

Abstract

Pose-graph optimization is becoming popular as a tool for solving position and attitude determination problems, especially in the context of Visual Simultaneous Localization and Mapping (V-SLAM). Recently proprioceptive information sources are appearing in this context, such as inertial measurement units and kinematic/dynamic models. These models require other quantities to be estimated along with camera poses and landmark 3D positions. Examples are IMU bias processes, friction coefficients and other process modeling parameters. In this work we propose a general approach to the estimation of time varying parameters in pose-graph optimization: we store parameter samples at arbitrary rate in auxiliary vertices and we employ interpolation schemes to recover their value at sensor readings timestamps. Prior knowledge or stochastic process models can be plugged in as additional edges incident in parameter nodes. Our approach is evaluated in the context of inertial navigation, where accelerometer and gyroscope bias processes need to be properly modeled and estimated.