Fast Image-Anomaly Mitigation for Autonomous Mobile Robots
This addresses image quality issues for autonomous mobile robots, enabling deployment with limited compute capabilities, though it appears incremental as it builds on existing anomaly mitigation methods.
The paper tackles the problem of camera anomalies like rain or dust degrading image quality for autonomous robots by implementing a real-time pre-processing step that mitigates artifacts, achieving state-of-the-art results with up to 40x faster inference time than existing approaches.
Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation. In this work we address this importantissue by implementing a pre-processing step that can effectivelymitigate such artifacts in a real-time fashion, thus supportingthe deployment of autonomous systems with limited computecapabilities. We propose a shallow generator with aggregation,trained in an adversarial setting to solve the ill-posed problemof reconstructing the occluded regions. We add an enhancer tofurther preserve high-frequency details and image colorization.We also produce one of the largest publicly available datasets1to train our architecture and use realistic synthetic raindrops toobtain an improved initialization of the model. We benchmarkour framework on existing datasets and on our own imagesobtaining state-of-the-art results while enabling real-time per-formance, with up to 40x faster inference time than existingapproaches.