Abstract: Obstacle detection is one of the tasks which are solved for intelligent robot in the unstructured complicated environment perception. Large amounts of training data are usually necessary in order to achieve satisfactory generalization, and attaining these training data is also relatively easy. While manually labeling data is an expensive and tedious process. The current research work related to the solutions of the above problems is also very limited. Active learning algorithm is introduced to obstacle detection here. Aiming at the problems and limitations in the process of applying general active learning algorithm, two strategies are used to improve general SVM active learning algorithm. These two strategies use a dynamic clustering to select the best representative samples and, according to the difference of experts labeling and current SVM classification results, to tune the SVM hyperplane location. At the same time, a new SVM active learning algorithm is proposed, that is KSVMactive. Experiments are carried out in real wilderness environment image database. Experimental results demonstrate: very good detection results are obtained using KSVMactiv algorithm with only 81 samples, which can show that it can significantly reduce the workload of labeling data, and its convergence is better than other active learning algorithms.