Aiming at the problem of front-end drift error accumulation of the V-SLAM algorithm, a visual odometry method based on optical imaging simulation was proposed. This method used the dense 3D point cloud data to perform Poisson reconstruction of the target surface and correlation of the physical properties of materials, and then built a physically based rendering engine (PBRT) according to optical physical properties and ray-tracing principle to generate simulated images of the target characteristics under different observation conditions. The simulated images of the target characteristics and the image captured by the optical camera were registered and the motion deviation was recovered, and the extended Kalman filter (EKF) was designed to output the optimal estimation value of the pose state. The prototype system development and experimental evaluation show that this method effectively overcomes the problem of drift error accumulation in the traditional methods and improves the front-end positioning accuracy by 56% compared to the traditional ORB-SLAM2 algorithm, which provides a new technical idea for the design of visual odometry.