This paper introduces an innovative keypoint detection technique based on Convolutional Neural Networks (CNNs) to enhance the performance of existing Deep Visual Servoing (DVS) models. To validate the convergence of the Image-Based Visual Servoing (IBVS) algorithm, real-world experiments utilizing fiducial markers for feature detection are conducted before designing the CNN-based feature detector. To address the limitations of fiducial markers, the novel feature detector focuses on extracting keypoints that represent the corners of a more realistic object compared to fiducial markers. A dataset is generated from sample data captured by the camera mounted on the robot end-effector while the robot operates randomly in the task space. The samples are automatically labeled, and the dataset size is increased by flipping and rotation. The CNN model is developed by modifying the VGG-19 pre-trained on the ImageNet dataset. While the weights in the base model remain fixed, the fully connected layer's weights are updated to minimize the mean absolute error, defined based on the deviation of predictions from the real pixel coordinates of the corners. The model undergoes two modifications: replacing max-pooling with average-pooling in the base model and implementing an adaptive learning rate that decreases during epochs. These changes lead to a 50 percent reduction in validation loss. Finally, the trained model's reliability is assessed through k-fold cross-validation.

Abstract-- This paper investigates the utility of Neural Radiance Fields (NeRF) models in extending the regions of operation of a mobile robot, controlled by Image-Based Visual Servoing (IBVS) via static CCTV cameras. Using NeRF as a 3Drepresentation prior, the robot's footprint may be extrapolated geometrically and used to train a CNN-based network to extract it online from the robot's appearance alone. The resulting footprint results in a tighter bound than a robot-wide bounding box, allowing the robot's controller to prescribe more optimal trajectories and expand its safe operational floor area. Visual servoing is a robotics technique that provides control Figure 1: [4] controls the robot based on its bounding box (yellow) based on visual feedback from external cameras. When checking if a trajectory [1], the field has evolved to encompass various methodologies is safe, its box must stay within the drivable region (blue).