Accurately estimating a vehicle's position and velocity in real-time is crucial for navigation, especially in environments where updates must be continuous and reliable. Achieving high-precision localization often requires integrating multiple positioning sources, particularly in indoor settings where satellite-based navigation is impractical. Visual-Inertial Odometry (VIO) systems are commonly employed for this purpose. With the rapid advancements in artificial intelligence and deep learning, particularly the deployment of deep networks on GPUequipped platforms, VIO systems have seen widespread adoption in recent years. However, these systems are vulnerable to remote attacks, such as laser-induced disruptions that can impair camera lenses, leading to compromised image capture and degraded visual localization. This paper introduces a highly-robust system utilizing a shallow convolutional neural network and a fully connected detection layer to enhance VIO resilience against such threats. Moreover, for power-sensitive applications, such as those relying on batteries, an Optimized Kalman Filter (OKF) is used to merge two distinct positioning sources, offering a more efficient alternative to recurrent neural networks like LSTMs. The proposed system demonstrates a 13.27% improvement in accuracy over existing robust VIO systems designed to counteract noise and distortion.
