VIVO: A Visual-Inertial-Velocity Odometry with Online Calibration in Challenging Condition

State estimation is a central component of autonomous navigation. To date, many methods presented have a disruptive potential for application, such as visual-inertial odometry (VIO), wheel and leg odometry (for short, body odometry). However, most of them are prone to fail in some challenging conditions like high-dynamic street scenes and sustain aggressive movements. To this end, in this paper, we present a novel visual-inertial-velocity odometry (VIVO) framework which incorporates velocity measurement provided by the proprioceptive sensing into the MSCKF-based VIO in a tightly coupled fashion. Furthermore, considering that the imprecise extrinsic parameters can severely undermine the state estimation performance, we hence perform VIVO along with online calibration of the body odometry’s extrinsic parameters by adding them to the estimated state vector. The generic VIVO can be deployed for a broad spectrum of robot models ranging from wheeled robots to legged robots. Both simulation and real-world experiments are performed to extensively validate the robustness and accuracy of the proposed method in challenging scenarios using wheeled and legged robot models, respectively.