SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation

2026-08-26

AI Summary

为解决复杂环境下传感器降级导致的定位问题，提出Super Odometry框架，通过分层自适应融合多种传感器数据，提高机器人在恶劣条件下的自主性和安全性。

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.

Problem

• resilient odometry
• environmental degradation
• autonomous systems
• sensor degradations

Innovation

• Hierarchical Adaptation
• Adaptive Feature Selection
• Inertial Odometry
• Sensor Fusion Framework