Performance of Image Enhancement Techniques on the 6D Pose Estimation Method under Complex Lighting Conditions

An approach for estimating 6D pose, which identifies location and orientation of the objects, plays an important role for intelligent robots to make appropriate responses when interacting with surrounding objects. In reality, intelligent robots often face complex lighting conditions, where the light intensity on objects is too high or too low. However, there are still few studies on 6D pose estimation under extreme lighting conditions. Therefore, we focus on this issue. Regarding complex lighting problems, the techniques of image enhancement are often added in the preprocessing stage to produce better quality input images. In this study, we apply several image enhancement techniques in the preprocessing stage of a 6D pose estimation method and empirically tests them on the standard dataset to determine which technique has the most influence on the performance of the method to estimating 6D pose. We apply the techniques for image enhancement, namely gamma correction (GC), contrast-limited adaptive histogram equalization (CLAHE), and histogram equalization (HE). We use the EfficientPose method which is state of the art in the study scope. The experimental results exhibit that the addition of image enhancement techniques in the preprocessing improves the performance of the EfficientPose method by up to 6.80% in high lighting condition and 8.30% in low lighting condition, where GC technique performance outperforms other techniques in high lighting condition and the CLAHE technique shows the best performance in low lighting condition. The results in the study provide important information that preprocessing techniques have a significant influence on the performance of the method for estimating 6D pose. Specifically, GC and CLAHE techniques provide the most optimal influence on the method in estimating 6D pose.