Lane detection and tracking algorithm based on active contour model was proposed to solve the poor robustness and realtime problem for vision navigation under factory or agricultural nonlinear illumination conditions. First of all, it was illustrated that navigation problem was equivalent to calculation of polynomial curve parameters, which could describe the navigation lanes. Secondly, the external energy function of active contour model was investigated, including three energy terms. The first energy term was about the Euclidean distance between lane colors and colors on one side of polynomial curve, by minimizing the first energy term could attract polynomial curve to navigation lanes. The second energy term was about the edge features, which could attract polynomial curve to lane edges. The third energy term was about the position difference of polynomial curve between adjacent frames, which could limit curve to change abruptly. Finally, the energy function was simplified to a nonlinear least squares problem, and the Gauss-Newton method as well as the Armijo-Goldstein inexact line search method were used to solve this problem. Home video and independent car were tested, the result showed that the algorithm achieved a navigation accuracy of 98.96% for both the straight lane and bending lane under nonlinear illumination, with average processing time of 40.18ms, and the independent car could walk along the navigation lane successfully. Experiment result showed that the algorithm was robust and real-time.