2026-08-26 · Sci. Robotics · 12 citations

SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation

Shibo Zhao, Sifan Zhou, Yuchen Zhang, Ji Zhang, Chen Wang, Wenshan Wang, Sebastian Scherer

Humanoids & locomotion Safety & evaluation

Published at Sci. Robotics (the arXiv record still lists it as a preprint). 12 citations, as of the last refresh.

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.

Ten-minute slide kit

Six slides is the whole talk: what was broken, what people tried, what these authors did, what the numbers say, where it falls over, and the sentence people should remember.

SLIDE 1

The problem

Odometry is an important technique to estimate the position and orientation of robots over time, while also allowing for the 3D geometry reconstruction of surrounding environments. It plays a crucial role in robotics, enabling spatial understanding and serving as a foundation for both high-level tasks such as navigation and exploration, and low-level functions like path planning and control [\[1\]](#page-15-0). As a result, odometry systems are widely used in robotic applications, including off-road driving…

SLIDE 2

What came before

Not recoverable from the parsed text. Read this section in the paper yourself.

SLIDE 3

The method

**Accuracy Evaluation** To further validate the precision of pose estimation, we conducted ATE[\[38\]](#page-15-30) analysis using our odometry system on the SubT-MRS dataset [\[39\]](#page-15-31). This dataset encompasses challenging environments featuring sensor degradation, aggressive locomotion, and extreme weather conditions. The eight sequences in the dataset are categorized into two groups for testing: Geometric degradation and Mixed degradation. ATE results of competing systems were sourced from an open…

SLIDE 4

What they measured

Real-world environments are often complex, involving multiple types of degradation ranging from mild to extreme. Therefore, an odometry solution should be adaptable and adjustable. <span id="page-2-0"></span> **Fig. 2. Evaluation of 13 types of degradation in a single run.** The color-coded trajectory depicts our estimated odometry of a legged robot navigating through over 13 complex degradation scenarios. Despite these difficulties, the final endpoint drift was only **20 cm** over a total distance of 2,966

SLIDE 5

Where it breaks

In this section, we provide insights on developing a robust odometry for degraded environments. **Hierarchical Adaptation is a Key Factor for Resilience** State estimation in challenging environments demands not only sensor redundancy but also computational efficiency. However, most existing odometry frameworks rely on rigid multi-modal fusion strategies that prioritize robustness by incorporating additional sensors, yet still fail to generalize across diverse degradation scenarios \[17, [41\]](#page-15-33). This…

SLIDE 6

One-line takeaway

Super Odometry elevates the inertial measurement unit to equal importance with camera and light detection and ranging (LiDAR) systems in the sensor fusion framework, providing a reliable fallback when exteroceptive sensors fail.

Assembled from the paper's own PDF, parsed with its layout intact so tables and equations survive, 122,534 characters of it, then split on the paper's own section headings. Extractive, not generated: every sentence here is lifted from the paper. Check it before you present it.