  Automatic detection of visual anomalies and changes in the environment has
been a topic of recurrent attention in the fields of machine learning and
computer vision over the past decades. A visual anomaly or change detection
algorithm identifies regions of an image that differ from a reference image or
dataset. The majority of existing approaches focus on anomaly or fault
detection in a specific class of images or environments, while general purpose
visual anomaly detection algorithms are more scarce in the literature. In this
paper, we propose a comprehensive deep learning framework for detecting
anomalies and changes in a priori unknown environments after a reference
dataset is gathered, and without need for retraining the model. We use the
SuperPoint and SuperGlue feature extraction and matching methods to detect
anomalies based on reference images taken from a similar location and with
partial overlapping of the field of view. We also introduce a self-calibrating
method for the proposed model in order to address the problem of sensitivity to
feature matching thresholds and environmental conditions. To evaluate the
proposed framework, we have used a ground robot system for the purpose of
reference and query data collection. We show that high accuracy can be obtained
using the proposed method. We also show that the calibration process enhances
changes and foreign object detection performance
