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arxiv: 2509.06593 · v2 · submitted 2025-09-08 · 💻 cs.RO

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling

Meher V.R. Malladi , Tiziano Guadagnino , Luca Lobefaro , Cyrill Stachniss This is my paper

Pith reviewed 2026-05-18 18:34 UTC · model grok-4.3

classification 💻 cs.RO
keywords LiDAR-inertial odometryrobust odometryscan-to-map registrationIMU integrationsensor fusionrobotic navigationodometry without modeling
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The pith

A LiDAR-inertial odometry system achieves robustness without sensor-specific modeling by using simplified IMU integration and scan-to-map registration with novel regularization.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a LiDAR-inertial odometry method that relies on a simplified motion model for integrating IMU data and registers LiDAR scans directly to a map. The approach incorporates a new regularization term during registration to enhance performance. It demonstrates that this setup maintains accuracy across different LiDAR sensors, platforms, and environments without any sensor-specific adjustments or retuning. Readers interested in robotic navigation would care because odometry underpins planning and control, and eliminating per-sensor engineering makes the system more general and easier to deploy in varied real-world conditions.

Core claim

The paper claims that by using only a simplified motion model for IMU integration and performing direct scan-to-map LiDAR registration with an added novel regularization, a single configuration of the odometry system produces accurate and robust results across a wide range of sensors and environments, including solid-state LiDARs on cars in urban areas and spinning LiDARs on handheld devices in natural settings.

What carries the argument

The central mechanism is the simplified IMU motion model combined with scan-to-map registration that supports a novel regularization term to constrain the LiDAR scan matching.

If this is right

  • Accurate odometry is obtained without iterative Kalman filtering or pre-integration in factor graphs.
  • The same system configuration works for both urban driving and unstructured natural environments.
  • Performance improves due to the regularization on LiDAR registration.
  • The open-sourced code enables integration into various navigation stacks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This could simplify the development of multi-sensor robotic systems by reducing the need for custom calibrations.
  • The regularization technique might be adaptable to other registration-based odometry methods.
  • In long-term deployments, this robustness could lead to fewer failures in changing conditions.

Load-bearing premise

The assumption that the simplified motion model and novel regularization together are sufficient to replace sensor-specific modeling and tuning while still delivering accurate odometry in all tested scenarios.

What would settle it

Observing significant drift or failure in odometry estimation on a previously untested LiDAR type or platform when using the exact same configuration would falsify the robustness without sensor-specific modeling.

Figures

Figures reproduced from arXiv: 2509.06593 by Cyrill Stachniss, Luca Lobefaro, Meher V.R. Malladi, Tiziano Guadagnino.

Figure 1
Figure 1. Figure 1: Our robust LiDAR-inertial odometry system is directly operational in different environments, sensor configurations, and robotic [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
read the original abstract

Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry system that does not rely on sensor-specific modeling. Sensor fusion techniques for LiDAR and inertial measurement unit (IMU) data typically integrate IMU data iteratively in a Kalman filter or use pre-integration in a factor graph framework, combined with LiDAR scan matching often exploiting some form of feature extraction. We propose an alternative strategy that only requires a simplified motion model for IMU integration and directly registers LiDAR scans in a scan-to-map approach. Our approach allows us to impose a novel regularization on the LiDAR registration, improving the overall odometry performance. We detail extensive experiments on a number of datasets covering a wide array of commonly used robotic sensors and platforms. We show that our approach works with the exact same configuration in all these scenarios, demonstrating its robustness. We have open-sourced our implementation so that the community can build further on our work and use it in their navigation stacks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes a robust LiDAR-inertial odometry system that uses a simplified motion model for IMU integration and direct scan-to-map registration with a novel regularization term. It claims to perform consistently across different LiDAR types (solid-state and spinning) and platforms without any sensor-specific modeling or configuration changes, supported by experiments on multiple datasets and an open-source implementation.

Significance. If the results hold, this approach could have high significance for robotics by enabling easier deployment of odometry in varied settings, reducing the need for expert tuning and sensor-specific adaptations. The multi-dataset validation with fixed parameters and open-sourcing are strengths that support reproducibility.

major comments (2)
  1. §4 (Method): The description of the novel regularization term in the scan-to-map registration is load-bearing for the robustness claim; however, the manuscript lacks a detailed mathematical formulation or motivation for how this term specifically compensates for the simplifications in the IMU motion model across sensor types.
  2. §5 (Experiments): While the paper reports consistent performance with identical configuration, the results section should include quantitative comparisons to baseline methods that use sensor-specific modeling to demonstrate the advantage, particularly in terms of accuracy metrics like ATE or RPE on each dataset.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive evaluation and recommendation of minor revision. The comments are constructive and we address each one below, indicating the changes we will make to strengthen the presentation of the method and results.

read point-by-point responses

  1. Referee: §4 (Method): The description of the novel regularization term in the scan-to-map registration is load-bearing for the robustness claim; however, the manuscript lacks a detailed mathematical formulation or motivation for how this term specifically compensates for the simplifications in the IMU motion model across sensor types.

    Authors: We agree that a more explicit mathematical treatment would improve clarity. In the revised manuscript we will expand the relevant subsection of §4 to include the full derivation of the regularization term together with a dedicated motivation paragraph. The added material will show how the term bounds residual errors arising from the simplified IMU integration in a manner that does not depend on LiDAR-specific characteristics, thereby supporting the sensor-agnostic claim. revision: yes

  2. Referee: §5 (Experiments): While the paper reports consistent performance with identical configuration, the results section should include quantitative comparisons to baseline methods that use sensor-specific modeling to demonstrate the advantage, particularly in terms of accuracy metrics like ATE or RPE on each dataset.

    Authors: We concur that direct numerical comparisons would further illustrate the practical benefit of avoiding sensor-specific modeling. In the revised §5 we will add ATE and RPE results against representative sensor-specific baselines on the same datasets, while keeping our own configuration fixed. We will also note the additional tuning effort required by the baselines to maintain fairness. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's core contribution is an alternative LiDAR-inertial odometry pipeline that uses a simplified IMU motion model plus direct scan-to-map registration with an added regularization term. This is presented as sufficient for robust performance without sensor-specific modeling or per-platform tuning. The derivation chain consists of high-level design choices justified by the resulting empirical performance across diverse datasets and platforms (all run with identical configuration). No equations or steps are shown that reduce a claimed prediction or first-principles result back to a fitted parameter or self-referential definition by construction. The approach is self-contained against external benchmarks via open-sourced code and multi-environment validation rather than internal circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach depends on the domain assumption that a simplified IMU motion model suffices and that the added regularization improves accuracy without introducing new biases or requiring per-sensor adjustments.

free parameters (1)
  • regularization parameter
    Weight or strength of the novel regularization term on LiDAR registration, introduced to improve performance.
axioms (1)
  • domain assumption A simplified motion model is adequate for IMU integration in the sensor fusion process.
    Explicitly stated as the only requirement for IMU data handling.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps

    cs.RO 2026-04 unverdicted novelty 5.0

    BIEVR-LIO improves robustness of LiDAR-inertial odometry by representing maps as voxel-wise oriented height images and sampling points only from geometrically informative regions.

Reference graph

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