Aiming at the difficulty in determining the motion parameters such as effective drive wheels and track slip rate of a polymetallic nodule mining vehicle, based on the modeling of traction force and load characteristics of tracks, a higher-order nonlinear system for estimating the motion parameters of a polymetallic nodule mining vehicle is proposed. To address the problem that the UKF based on Gaussian model cannot achieve high estimation accuracy in high-order nonlinear systems, an adaptive traceless particle filtering algorithm (AUPF) based on Monte Carlo sam? pling principle is proposed，and the adaptive traceless Kalman filter (AUKF) is used to improve the Particle Filter (PF) by refining the probability density function, which solves the shortcomings that the PF is easy to diverge and the estimation accuracy of UKF is low. The experimental results show that the AUPF algorithm can achieve an accurate estimation of the motion parameters of polymetallic nodule mining vehicles, and there is an important engineering ap? plication value.