Robust Odometry for Wheeled Robots in Smoky Environments: Leveraging Smoke-Adaptive Image Features and Multisensor Tight Coupling

Abstract

Odometry provides state estimation for the robot, which is crucial for enabling autonomous mobility in firefighting robots. However, in visually degraded environments such as haze and smoke, conventional odometry is prone to failure. In this study, we propose a visual-inertial-Wheel encoders odometry system, VIW-Haze, specifically designed for firefighting robots. This system integrates proprioceptive sensors (encoders, inertial measurement units) with visual odometry. To address the interference caused by smoke and haze, we designed a feature extraction network with a smoke-adaptive module. Additionally, we proposed a method to tightly integrate encoder measurements by modeling the slip rate as a scale factor. We incorporated slip-adjusted encoder measurements during initialization, reducing the system’s dependence on motion excitation and parallax. Finally, we conducted extensive testing on various datasets to validate our approach. The results indicate that the feature extraction network can effectively detect feature points in smoke images, demonstrating excellent keypoint repeatability. Additionally, due to the real-time estimation of slip, our odometry outperforms traditional methods, with accuracy improvements exceeding 20%. It also remains stable even when visual input fails entirely. Importantly, this approach is not limited to firefighting robots; it can provide reliable state estimation for any ground robot operating in visually degraded environments.