In the study of 3-Dimensional (3D) local feature descriptor, it is difficult to strike a balance among accuracy, matching time and memory consumption. To solve this problem, an adaptive binary simplification method for 3D feature descriptor was proposed based on the standard deviation principle in statistical theory. First, different binary feature descriptors were generated by changing the binarization unit length and the number of standard deviations in the simplification model, which were applied into the currently widely used Signature of Histogram of OrienTations (SHOT) descriptor, and the optimal combination of binarization unit length and the number of standard deviations was determined by experiments. Finally, the simplified descriptor under the optimal combination was named Standard Deviation feature descriptor for Signature of Histogram of OrienTations (SD-SHOT). Experimental results show that compared with the SHOT descriptor without simplification, SD-SHOT reduces the key point matching time to 1/15 times and the memory occupancy to 1/32 times of SHOT; compared with the existing mainstream simplification methods such as Binary Feature Descriptor for Signature of Histogram of OrienTations (B-SHOT), SD-SHOT has the optimal comprehensive performance. In addition, the validity of the proposed method is verified in the actual robot sorting scene consisting of five different categories of objects.