Divergent trinocular vision observers design for extended Kalman filter robot state estimation

Abstract

Here, we report the design of two deterministic observers that exploit the capabilities of a home-made divergent trinocular visual sensor to sense depth data. The three-dimensional key points that the observers can measure are triangulated for visual odometry and estimated by an extended Kalman filter. This work deals with a four-wheel-drive mobile robot with four passive suspensions. The direct and inverse kinematic solutions are deduced and used for the updating and prediction models of the extended Kalman filter as feedback for the robot’s position controller. The state-estimation visual odometry results were compared with the robot’s dead-reckoning kinematics, and both are combined as a recursive position controller. One observer model design is based on the analytical geometric multi-view approach. The other observer model has fundamentals on multi-view lateral optical flow, which was reformulated as nonspatial–temporal and is modeled by an exponential function. This work presents the analytical deductions of the models and formulations. Experimental validation deals with five main aspects: multi-view correction, a geometric observer for range measurement, an optical flow observer for range measurement, dead-reckoning and visual odometry. Furthermore, comparison of positioning includes a four-wheel odometer, deterministic visual observers and the observer–extended Kalman filter, compared with a vision-based global reference localization system.

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“…e Kalman filter is an algorithm for linear minimum variance error estimation of the state sequence of a dynamic system. e discrete Kalman filter evaluates the process using a form of feedback control: the process state is evaluated on a temporal filter, and feedback is obtained through (noisy) measurements [25,26].…”

A new filtering strategy for target tracking application using the second form of Smooth Variable Structure Filter

“…[12][13][14] This filter can be complicated to implement and tune, and it is only suitable for systems that are smoothly nonlinear. [15][16][17][18][19] To overcome these limitations in the case of target tracking, the Unscented Kalman Filter (UKF) is proposed, which is based on sigma points to approximate the nonlinearities on the states, and its process does not require linearization of the state model. 20 However, the UKF is a more complex task in terms of computational time.…”