An adaptive image illumination partitioning and threshold segmentation approach based on a model of illumination and color was proposed for path extraction in the field of view for a vision-guided AGV under complex illumination conditions. Firstly, the relation between light illumination and image brightness was analyzed, and the correlation model of illumination and color was built by measuring color distribution with respect to different illuminations in images under complex illumination conditions. Secondly, the image of a guide path was partitioned into different illumination regions according to the model of illumination and color. Then the image of low-illumination region was enhanced in the space of RGB color to retrieve the color information of the guide path, and the image of high-illumination region was processed by differentiating chrominance components of Cb and Cr to suppress the common-mode luminance interference. Finally, an adaptive threshold segmentation method was performed for different illumination regions. A large number of experimental results showed that this path extraction approach had high adaptability to complex illumination when recognizing the guide path in the vision field with both high-reflective and dark-shadow regions caused by the environment illumination, and it achieved the recognition accuracy of 98% owing to decreasing the errors of under-segmentation and over-segmentation in high-reflective and dark-shadow regions significantly.