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The visual SLAM method is widely used for self-localization and mapping incomplex environments. Visual-inertia SLAM, which combines a camera with IMU,can significantly improve the robustness and enable scale weak-visibility,whereas monocular visual SLAM is scale-invisible. For ground mobile robots, theintroduction of a wheel speed sensor can solve the scale weak-visible problemand improve the robustness under abnormal conditions. In this thesis, amulti-sensor fusion SLAM algorithm using monocular vision, inertia, and wheelspeed measurements is proposed. The sensor measurements are combined in atightly coupled manner, and a nonlinear optimization method is used to maximizethe posterior probability to solve the optimal state estimation. Loop detectionand back-end optimization are added to help reduce or even eliminate thecumulative error of the estimated poses, thus ensuring global consistency ofthe trajectory and map. The wheel odometer pre-integration algorithm, whichcombines the chassis speed and IMU angular speed, can avoid repeatedintegration caused by linearization point changes during iterativeoptimization; state initialization based on the wheel odometer and IMU enablesa quick and reliable calculation of the initial state values required by thestate estimator in both stationary and moving states. Comparative experimentswere carried out in room-scale scenes, building scale scenes, and visual lossscenarios. The results showed that the proposed algorithm has high accuracy,2.2 m of cumulative error after moving 812 m (0.28%, loopback optimizationdisabled), strong robustness, and effective localization capability even in theevent of sensor loss such as visual loss. The accuracy and robustness of theproposed method are superior to those of monocular visual inertia SLAM andtraditional wheel odometers.

Robust tightly coupled pose estimation based on monocular vision, inertia, and wheel speed