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arxiv: 2606.31019 · v1 · pith:32WZJM7Dnew · submitted 2026-06-30 · 💻 cs.RO

Ground Plane-Aided Extrinsic Calibration of Inertial and RGB-D Sensors for Uncrewed Aerial Vehicles

Ilyar Asl Sabbaghian Hokmabadi , Mahdis Bisheban This is my paper

Pith reviewed 2026-07-01 05:54 UTC · model grok-4.3

classification 💻 cs.RO
keywords extrinsic calibrationIMURGB-D cameraUAVtargetless calibrationground planefloor segmentationsensor alignment
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The pith

A targetless method calibrates IMU and RGB-D sensors on UAVs by aligning the floor normal with the gravity vector.

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

The paper develops a calibration approach for inertial measurement units and RGB-D cameras mounted on uncrewed aerial vehicles that requires no special targets or initial parameter guesses. It applies deep learning to identify the floor in depth images, computes the normal vector to that plane, and uses the fact that this normal aligns with gravity as sensed by the accelerometer to solve for the relative orientation between the sensors. This matters for UAVs because precise sensor alignment improves trajectory estimation during flight, and current methods often depend on checkerboard patterns or manual setup. The results show the new approach beats standard MATLAB calibration tools and performs on par with the Kalibr toolbox while avoiding extra equipment.

Core claim

By segmenting the ground plane from RGB-D depth data using deep learning and estimating its normal vector, the method combines this with the gravity direction from the IMU accelerometer in a robust estimator to recover the extrinsic rotation between the two sensors without any planar targets or prior estimates.

What carries the argument

The robust estimation step that aligns the estimated floor normal vector from segmented depth points with the gravity vector measured in the accelerometer frame.

If this is right

  • Calibration becomes possible in ordinary environments without carrying checkerboard targets to the field.
  • The method supports accurate trajectory estimation for UAVs by providing reliable extrinsic parameters.
  • Performance exceeds that of MATLAB calibration toolboxes while matching specialized target-based software.
  • Setup time decreases since no initial guesses or specialized equipment are needed.

Where Pith is reading between the lines

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

  • If floor segmentation remains reliable across different surfaces and lighting, the approach could apply to ground robots as well.
  • Combining this with visual features might handle cases where the floor is partially obscured during flight.
  • Real-time implementations could support on-the-fly recalibration during UAV missions.

Load-bearing premise

Deep-learning-based floor-segmentation reliably extracts ground points from the depth channel of RGB-D images under typical UAV operating conditions.

What would settle it

Collect flight data from a UAV with both this method and Kalibr using a checkerboard, then compare the resulting rotation parameters; a difference exceeding 2 degrees on any axis would show the targetless method fails to match established accuracy.

Figures

Figures reproduced from arXiv: 2606.31019 by Ilyar Asl Sabbaghian Hokmabadi, Mahdis Bisheban.

Figure 2
Figure 2. Figure 2: The sensors are connected to an onboard computer where the incoming measurements are time-synchronized. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
read the original abstract

Accurate extrinsic calibration of inertial sensors, such as Inertial Measurement Units (IMUs) and cameras is crucial for trajectory estimation of Uncrewed Aerial Vehicles (UAVs). While numerous calibration methods have been proposed, these techniques often rely on specialized equipment, planar targets, and an initial estimate of the calibration parameters. In this research, we propose a targetless calibration method designed for UAVs equipped with IMUs and RGB-Depth (RGB-D) cameras. Our approach leverages deep-learning-based floor-segmentation to extract ground points from the depth channel of RGB-D images. Subsequently, the normal vector to these points is estimated. The known orientation of the normal to the floor segment and the gravity vector sensed in the accelerometer's frame are utilized in a robust estimation approach to estimate the extrinsic calibration parameters. We illustrate that the developed method outperforms MATLAB's Toolboxes and exhibits similar performance to Kalibr without the use of specialized checkerboard targets.

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 manuscript proposes a targetless extrinsic calibration method for IMU and RGB-D sensors on UAVs. It uses deep-learning-based segmentation of the floor in RGB-D depth images to extract ground points and estimate the plane normal vector, which is then combined with the gravity vector measured by the accelerometer in a robust estimation procedure to recover the extrinsic parameters. The central claim is that the resulting calibration outperforms MATLAB toolboxes and achieves performance comparable to Kalibr without requiring checkerboard targets.

Significance. If the performance claims are substantiated with quantitative evidence, the approach would provide a practical targetless calibration pipeline that exploits the ground plane commonly available during UAV operation, reducing reliance on specialized hardware and thereby simplifying deployment in field robotics settings.

major comments (2)
  1. [Abstract] Abstract: the assertion that the method 'outperforms MATLAB's Toolboxes and exhibits similar performance to Kalibr' is presented without any quantitative metrics, error bars, test conditions, or dataset details, rendering the central performance claim impossible to evaluate.
  2. [Method] Method (floor-segmentation step): the normal-vector estimate used for calibration is obtained solely from deep-learning segmentation of ground points in the RGB-D depth channel. No quantitative assessment of segmentation accuracy, normal angular error, or robustness under UAV-typical conditions (lighting variation, texture, motion blur) is supplied, yet this step is load-bearing for all reported accuracy results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment point by point below and will revise the manuscript accordingly where the concerns are valid.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that the method 'outperforms MATLAB's Toolboxes and exhibits similar performance to Kalibr' is presented without any quantitative metrics, error bars, test conditions, or dataset details, rendering the central performance claim impossible to evaluate.

    Authors: We agree that the abstract summarizes the performance claims without supporting quantitative details. In the revised manuscript we will expand the abstract to include key metrics (mean rotation/translation errors and standard deviations versus the baselines), error bars, test conditions, and dataset information so that the claims can be directly evaluated. revision: yes

  2. Referee: [Method] Method (floor-segmentation step): the normal-vector estimate used for calibration is obtained solely from deep-learning segmentation of ground points in the RGB-D depth channel. No quantitative assessment of segmentation accuracy, normal angular error, or robustness under UAV-typical conditions (lighting variation, texture, motion blur) is supplied, yet this step is load-bearing for all reported accuracy results.

    Authors: The manuscript validates the pipeline via end-to-end calibration accuracy, which implicitly depends on the segmentation quality. We acknowledge the absence of standalone segmentation metrics. In revision we will add quantitative results on segmentation accuracy (e.g., IoU) and normal-vector angular error computed on our datasets. Robustness discussion will be expanded using the lighting and texture conditions present in the collected UAV sequences; exhaustive additional tests for motion blur would require new experiments beyond the current scope. revision: partial

Circularity Check

0 steps flagged

No circularity in derivation; method is self-contained empirical proposal

full rationale

The paper describes a targetless extrinsic calibration pipeline that extracts ground points via deep-learning segmentation, estimates the floor normal, and combines it with the accelerometer gravity vector in a robust estimator. No equations, parameter-fitting steps, or derivations appear in the abstract or description. Performance claims are direct empirical comparisons to MATLAB and Kalibr rather than any 'prediction' that reduces to fitted inputs. No self-citations, uniqueness theorems, or ansatzes are invoked. The central claim therefore rests on external validation data rather than internal construction, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract supplies no explicit free parameters or invented entities. The method rests on the domain assumption that the floor is a flat horizontal plane whose normal aligns with gravity.

axioms (1)
  • domain assumption The floor is a flat horizontal plane whose normal vector aligns with the gravity direction.
    This alignment is required to equate the estimated depth normal with the accelerometer gravity vector for rotation estimation.

pith-pipeline@v0.9.1-grok · 5704 in / 1078 out tokens · 21676 ms · 2026-07-01T05:54:15.078131+00:00 · methodology

discussion (0)

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