2026-05-25原文

Four Simple Proprioceptive Estimators for Legged Robots

This paper presents four progressively complex state estimators for legged robots that use foot-contact information to mitigate IMU drift, including a contact-aided invariant EKF, factor graph, fixed-lag smoother with contact-episode footholds, and a variant with evolving IMU bias. Implementations are available in GTSAM and ROS2.

Article intelligence

EngineersAdvanced

Key points

Legged robots suffer from IMU drift; foot contacts can help correct it.
Four state estimators of increasing complexity are developed, from EKF to fixed-lag smoother.
All variants are implemented in GTSAM with ROS2 compatibility.
Experiments show contact aiding significantly improves localization accuracy.

Why it matters

This matters because legged robots suffer from IMU drift; foot contacts can help correct it.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

[2605.23100] Four Simple Proprioceptive Estimators for Legged Robots

[Submitted on 21 May 2026]

Title:Four Simple Proprioceptive Estimators for Legged Robots

View a PDF of the paper titled Four Simple Proprioceptive Estimators for Legged Robots, by Frank Dellaert and 3 other authors

View PDF HTML (experimental)

Abstract:Legged robots carry an IMU, but the inertial solution drifts because consumer-grade IMUs are noisy. However, the feet create intermittent contacts with the environment that can be used to mitigate that drift. This report develops a sequence of increasingly expressive legged robot state estimators that leverage this. In all cases, the floating-base state comprises attitude, position, velocity, and IMU biases. To model foot contacts, we start from the contact-aided invariant EKF of Hartley et al., albeit at a reduced contact update rate. This is then augmented by replacing the measurement update by a small factor graph. Finally, we turn the same factors into a fixed-lag smoother with contact-episode footholds, with and without an evolving IMU bias. To facilitate reproducibility and further research in proprioceptive legged odometry, all four variants are available in GTSAM (Dellaert et. al), and we additionally provide a ROS2-compatible implementation.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2605.23100 [cs.RO]

(or arXiv:2605.23100v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2605.23100

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Varun Agrawal [view email] [v1] Thu, 21 May 2026 23:17:48 UTC (858 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled Four Simple Proprioceptive Estimators for Legged Robots, by Frank Dellaert and 3 other authors

View PDF

HTML (experimental)

TeX Source

view license

Current browse context:

cs.RO

new | recent | 2026-05

Change to browse by:

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)