To further enhance the novelty detection capabilities of the EPSNoDe framework, additional sensor modalities or data sources can be combined with UWB signal characteristics. One potential modality is visual data from cameras or depth sensors, which can provide complementary information about the environment's structure and obstacles. By fusing visual data with UWB signal data, the framework can create a more comprehensive representation of the environment, enabling more accurate detection of novelties and changes.
In addition to visual data, inertial sensors such as accelerometers and gyroscopes can be integrated to provide information about the robot's motion and orientation. By combining inertial sensor data with UWB signal characteristics, the framework can better understand the robot's movement patterns and how they relate to changes in the environment. This multi-sensor fusion approach can improve the framework's ability to detect anomalies and adapt to dynamic environmental conditions effectively.
Furthermore, environmental data such as temperature, humidity, or air quality measurements can also be incorporated to provide context about the surroundings. By integrating diverse sensor modalities and data sources, the EPSNoDe framework can create a more robust and comprehensive model for novelty detection, enhancing the reliability of robot localization in evolving environments.