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Nowadays, more and more sensors are equipped on robots to increase robustnessand autonomous ability. We have seen various sensor suites equipped ondifferent platforms, such as stereo cameras on ground vehicles, a monocularcamera with an IMU (Inertial Measurement Unit) on mobile phones, and stereocameras with an IMU on aerial robots. Although many algorithms for stateestimation have been proposed in the past, they are usually applied to a singlesensor or a specific sensor suite. Few of them can be employed with multiplesensor choices. In this paper, we proposed a general optimization-basedframework for odometry estimation, which supports multiple sensor sets. Everysensor is treated as a general factor in our framework. Factors which sharecommon state variables are summed together to build the optimization problem.We further demonstrate the generality with visual and inertial sensors, whichform three sensor suites (stereo cameras, a monocular camera with an IMU, andstereo cameras with an IMU). We validate the performance of our system onpublic datasets and through real-world experiments with multiple sensors.Results are compared against other state-of-the-art algorithms. We highlightthat our system is a general framework, which can easily fuse various sensorsin a pose graph optimization. Our implementations are opensource\footnote{https://github.com/HKUST-Aerial-Robotics/VINS-Fusion}.

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors