Fast Image-Anomaly Mitigation for Autonomous Mobile Robots

TL;DR

This paper presents a real-time image anomaly mitigation method for autonomous robots, using a shallow adversarial generator with enhancement, trained on a large dataset with synthetic rain, achieving state-of-the-art results with significantly faster inference.

Contribution

A novel real-time anomaly mitigation framework with a shallow generator and enhancement, trained on a large synthetic dataset, enabling deployment on resource-limited autonomous systems.

Findings

Achieves up to 40x faster inference than existing methods.

Provides state-of-the-art anomaly mitigation performance.

Supports real-time processing for autonomous robot applications.

Abstract

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.