In order to enable rescue robots to accurately detect human joint points in dangerous building environments to replace rescuers into dangerous scenes. A machine learning based human joint point detection method was proposed. Firstly, human joint detection data was obtained through Kinect cameras, and normalized methods were used to smooth and preprocess the joint data. Secondly, human joint length constraints and joint rotation angle constraints were added to the preprocessed data to generate the spatial positions of each human joint point. Finally, the Tsai method was used for hand eye calibration to obtain the distance between the robotic arm and the injured person. The experimental results show that the method accurately measures the position of the casualty joint point in the rescue environment of a dangerous building, which verifies the effectiveness of the proposed detection method.