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This research introduces Super Odometry 2.0, a sensor fusion framework that enhances robotic autonomy by dynamically adapting to severe environmental degradations like smoke or snow. By elevating inertial measurement units (IMUs) to parity with cameras and LiDAR through hierarchical adaptation, the system ensures reliable navigation when exteroceptive sensors fail. The approach represents a significant advancement in robust motion estimation for diverse robotic platforms operating under extreme conditions.
SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation
arXiv:2608.25427v1 Announce Type: new Abstract: Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle with severe sensory degradations and extreme conditions such as smoke, sandstorms, snow, or low-light conditions, threatening both the safety and functionality of robots. To address these challenges, we present Super Odometry, a sensor fusion framework that dynamically adapts to varying levels of environmental degradation. Super Odometry employs a hierarchical structure to integrate four core modules from lower-level to higher-level adaptability including adaptive feature selection, adaptive state direction selection, adaptive engine selection, and a novel learning- based inertial odometry. The inertial odometry, trained on over 100 hours of heterogeneous robotic platforms, captures comprehensive motion dynamics. Super Odometry elevates the inertial measurement unit (IMU) to equal importance with camera and LiDAR within the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail. Super Odometry has been validated across 200 kilometers and 800 operational hours on a fleet of aerial, wheeled, and legged robots, under diverse sensor configurations, environmental degradation, and aggressive motion profiles. It marks an important step towards safe and long-term robotic autonomy in all-degraded environments.
Title:SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation
Abstract:Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle with severe sensory degradations and extreme conditions such as smoke, sandstorms, snow, or low-light conditions, threatening both the safety and functionality of robots. To address these challenges, we present Super Odometry, a sensor fusion framework that dynamically adapts to varying levels of environmental degradation. Super Odometry employs a hierarchical structure to integrate four core modules from lower-level to higher-level adaptability including adaptive feature selection, adaptive state direction selection, adaptive engine selection, and a novel learning- based inertial odometry. The inertial odometry, trained on over 100 hours of heterogeneous robotic platforms, captures comprehensive motion dynamics. Super Odometry elevates the inertial measurement unit (IMU) to equal importance with camera and LiDAR within the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail. Super Odometry has been validated across 200 kilometers and 800 operational hours on a fleet of aerial, wheeled, and legged robots, under diverse sensor configurations, environmental degradation, and aggressive motion profiles. It marks an important step towards safe and long-term robotic autonomy in all-degraded environments.
Robotics (cs.RO)
arXiv:2608.25427 [cs.RO]
(or arXiv:2608.25427v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.25427 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1126/scirobotics.adv1818 Focus to learn more DOI(s) linking to related resources
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