Aiming at the problems of slow manual palletizing, high risk and easy damage to products after the assembly of microchannel heat exchanger, a palletizing system based on 3D visual guidance is presented in this paper. Firstly, an automatic calibration method for RGB-D camera extrinsic parameters was adopted: used random sample consensus algorithm RANSAC for plane fitting, and the pallet was idealized as a plane. The camera extrinsic parameters were calculated according to the rotation relationship between the plane normal vector and the camera coordinate system. When it was performing detection, converted depth data into point cloud data and preprocessed, including coordinate transformation, downsampling, etc. After that, according to the characteristics of stacking, a point cloud segmentation algorithm based on depth and the number of points was used, which could not only segment the pallet and sponge strips, but also effectively detect obstacles. For different parts segmented, the pallet was positioned by fitting the minimum bounding rectangle, and the stacking depths were obtained by calculating the centroid of the sponge strips. Finally, a series of constraints were used to determine whether the unloading conditions were met. The experimental results show that the automatic calibration is effective, the time consuming is about 0.43 s, and the depth range of the pallet is 11 mm after calibration. The algorithm can accurately identify various situations above the stacking and the time consuming is about 1.63 s.