Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework

Andrii Chumak; Dominik Kulmer; Ilir Tahiraj; Markus Lienkamp

REVIEW 4 major objections 4 minor 38 references

Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Multi-LiCa calibrates multiple LiDARs automatically and targetless, with no initial pose guess, from partially overlapping views.

desk verdict A genuinely useful cascading-overlap calibration idea and clean open-source implementation, but the empirical case is thin and the 0.2 fitness gate is the load-bearing assumption that needs real validation. read the letter →

arxiv 2501.11088 v1 pith:VRZWFEJS submitted 2025-01-19 cs.RO

Dominik Kulmer , Ilir Tahiraj , Andrii Chumak , Markus Lienkamp This is my paper · ORCID

classification cs.RO

keywords LiDARextrinsiccalibrationmulti-LitargetlesspointcloudregistrationFPFHTEASER++GICPautonomousvehicles

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Multi-LiCa is a targetless, motionless extrinsic calibration method for multi-LiDAR systems that requires no initial transformation estimate, no extra sensors, and no calibration targets. It aligns each LiDAR pair through a coarse feature-based step using FPFH/TEASER++, then refines with GICP, and iteratively merges successfully aligned clouds into a growing target cloud so that sensors without a direct field-of-view overlap can be calibrated through a chain of overlaps. The authors evaluate on a four-LiDAR automotive setup (EDGAR) and a four-LiDAR heterogeneous dataset (HeLiPR), reporting that Multi-LiCa is on par with or better than the CROON baseline in accuracy while computing each calibration in about 3 seconds on a single CPU core. If this holds, the method removes a significant manual step from deploying multi-sensor autonomous vehicles and extends calibration to unusual sensor layouts where no single LiDAR sees all others.

What carries the argument

The load-bearing mechanism is a two-stage registration pipeline followed by a greedy merging loop. FPFH descriptors computed on voxelized clouds are matched by TEASER++ to produce a coarse transformation without an initial guess; GICP then refines each source–target pair. The GICP fitness score, defined as the number of inlier correspondences divided by the total points in the target cloud, decides which alignment is accepted, using a threshold of 0.2. Accepted source clouds are transformed and merged into the growing target cloud, and all calibrations involving the newly merged sensor are recomputed, which is what lets sensors with only indirect overlap to the target be calibrated.

What would settle it

Run Multi-LiCa on a rig where one sensor's field of view is completely disjoint from all others, so no overlap chain reaches the target, and observe that the algorithm terminates without calibrating that sensor; or, using two clouds with a known transform, plant a wrong alignment with a GICP fitness above 0.2 and check whether it is merged and propagated.

Watch

Extended reading notes

Core claim

The central claim is that the full multi-LiDAR extrinsic calibration problem can be solved automatically under a connected-overlap assumption: every LiDAR must share a field of view, directly or transitively, with a chosen target LiDAR. The pipeline computes a coarse transformation between every pair of voxelized point clouds using FPFH features matched by TEASER++, obtaining a global alignment without any user-provided initial guess. Each pair is then fine-registered with GICP, and the most successful alignment, measured by a fitness score of at least 0.2, is merged into the growing target cloud; all remaining pairs involving the newly merged sensor are recomputed against the merged cloud. This greedy iterative merge allows a sensor like EDGAR's rear LiDAR, which has no direct overlap with any other single sensor in the worst case, to be calibrated through intermediate clouds. The authors claim that on the EDGAR and HeLiPR datasets this approach is on par with or more accurate than the existing CROON method, especially for setups with angled, non-overlapping LiDARs, and is about three times faster.

Load-bearing premise

The method assumes that every LiDAR's field of view overlaps with the target either directly or through a chain of pairwise overlaps, so that the iterative merging can eventually include every sensor; a LiDAR with no overlap to any already-calibrated cloud terminates the algorithm without being calibrated.

Editorial extensions

If this is right

Multi-LiDAR rigs with partial or chain-connected field-of-view overlaps can be calibrated automatically, without targets, motion, or initial pose estimates, as long as every sensor has a direct or transitive overlap path to the target LiDAR.
The coarse-to-fine design with FPFH/TEASER++ and GICP removes the need for manual initialization, which the paper shows is critical for angled setups like EDGAR where existing methods cannot align the rear LiDAR.
Because the iterative merge recomputes alignments against growing clouds, the method scales to any number of sensors with a single parameter set, and the paper demonstrates this on two four-LiDAR setups with different sensor types and scan patterns.
The optional LiDAR-to-ground calibration step computes roll, pitch, and vertical offset to a base frame from a stationary, flat ground plane, further automating the sensor-to-vehicle calibration.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The greedy merge policy makes calibration order-dependent: the first accepted high-fitness pair biases the growing cloud, so a different initial pairing could yield a different final calibration; a global consistency check across pairwise transformations would make the result more repeatable.
The fixed fitness threshold of 0.2 could admit a wrong alignment in symmetric or feature-poor scenes, so a validation step that re-checks the final multi-cloud consistency or rejects merges whose relative pose disagrees with other pairwise estimates would add robustness.
The coarse stage's choice of FPFH/TEASER++ over RANSAC-based feature matching is motivated by robustness; comparing the coarse stage against learned registration methods would clarify the accuracy/runtime trade-off for future setups.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things about Multi-LiCa. First, the core idea is genuinely new: instead of requiring every LiDAR to overlap the target, it iteratively merges clouds into a growing target, so sensors with only indirect overlap (A->B->C->T) can be calibrated with no initial guess. That is a real contribution, and the code is on GitHub. Second, the evaluation is not strong enough to back the headline claims of generality. Parameters were optimized on the same HeLiPR and EDGAR scenes that later appear in the results, so the reported accuracy is in-sample. Only one baseline (CROON) is compared, and it fails outright on EDGAR, which makes the comparison look lopsided. The HeLiPR ground truth is admittedly imperfect, and the EDGAR ground truth is a manual RVIZ alignment with no uncertainty, so the absolute error bars are shaky.

The biggest soft spot is the fitness threshold. Algorithm 1 decides that a merge is correct if the Open3D GICP fitness exceeds 0.2. That number is presented as 'found sufficient' with no sensitivity analysis, no distribution of fitness for correct versus incorrect alignments, and no report of actual margins. Because a merged cloud becomes the reference for downstream LiDARs, a single false positive at 0.2 propagates through the whole chain. The stress-test note is right: this is an unvalidated load-bearing gate. That said, the paper explicitly states the connected-FOV assumption and admits that more testing is needed, so this is not a hidden flaw. It is an under-supported claim.

What the paper does well: the writing is clear, the method is plausible, the algorithm pseudocode is concrete, and the code release is a practical enabler. For someone integrating a multi-LiDAR rig with non-overlapping views, this is a useful tool and a fair starting point. The cascading-merge strategy is worth citing, and the LiDAR-to-ground calibration trick is a nice minor addition.

My recommendation: send it to peer review, but require out-of-sample evaluation, at least one baseline that actually works on the target setup, uncertainty quantification for ground truth, and a real sensitivity analysis of the fitness threshold. The paper deserves referee time because the idea and code are valuable, but the current evidence is conditional at best.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes Multi-LiCa, an automatic, motion- and targetless extrinsic calibration framework for multiple LiDARs. The method first computes coarse alignment using FPFH features on voxelized clouds and TEASER++, then refines with GICP. The key algorithmic contribution is the iterative merging strategy in Algorithm 1: pairwise GICP fitness scores are computed for all available source–target pairs, the pair with the highest fitness is accepted if its score exceeds 0.2, the source cloud is transformed and merged into the growing target cloud, and the process repeats until all LiDARs are calibrated or no pair passes the threshold. This allows calibration of sensors that lack direct FOV overlap by cascading overlaps. The approach is evaluated on ten scenes from the HeLiPR dataset and ten scenes from the EDGAR vehicle, with CROON as the only baseline, and errors are reported as RMSEs in translation and rotation relative to the available ground truth. The paper also includes a LiDAR-to-ground calibration method and states that the source code is available.

Significance. The manuscript addresses a practically relevant problem: calibrating multiple LiDARs with heterogeneous sensing characteristics and only partial or indirect FOV overlap, without manual initial guesses or external targets. The main strengths are the removal of manual initialization, the iterative cloud-merging strategy that extends calibration beyond direct overlap, the explicit statement of the connected-FOV assumption, and the availability of source code. If the reported results were established out-of-sample, the framework would be a useful open-source contribution to the multi-LiDAR calibration literature. However, the central success gate (the fitness threshold), the parameter tuning procedure, and the ground-truth references are not yet validated to the level needed for the quantitative claims; the evaluation is also limited to twenty scenes and a single baseline. These issues are load-bearing for the abstract's claims of generalizability and parity or superiority to existing methods.

major comments (4)

[Section III.B, Algorithm 1] The decision to accept a merge, and the termination of the whole algorithm, rest entirely on the Open3D GICP fitness score exceeding 0.2, but the manuscript provides no evidence that fitness separates correct from incorrect alignments. Fitness is the ratio of inlier correspondences to target points, i.e., a coverage statistic rather than a correctness certificate; a wrong local minimum with plausible correspondences can exceed 0.2, and because the accepted source cloud is merged into the reference for all subsequently calibrated LiDARs, a single false positive can cascade through the overlap chain. Conversely, a correct low-overlap pair may score below 0.2 and cause premature termination. Please report the distribution of fitness for correct versus incorrect alignments, the actual fitness margins on accepted pairs, and a sensitivity analysis of the threshold, preferably on data not used for tuning.
[Section III.D and Section IV] The parameters, including voxel size, FPFH radius, GICP settings, and the fitness threshold, were optimized on the same HeLiPR and EDGAR scenes that later appear in the results. The reported accuracy is therefore an in-sample fit rather than an out-of-sample prediction, which weakens the paper's generalizability claim. Please either tune on a separate subset and report test-set results, or show that the results are insensitive to parameter variations over a plausible range, including threshold values around 0.2.
[Section IV.A and Section IV.B] The two ground-truth references are not adequate for the reported absolute RMSEs. For HeLiPR, the paper states that there is a substantial deviation between the ground-truth pose and the data for the Aeva LiDAR and that the values "have to be looked at in relative terms," yet the reported errors are absolute RMSEs against that same ground truth. For EDGAR, the ground truth comes from a manual alignment of 0.5 m cubes in RVIZ with no uncertainty estimate. Please report relative error comparisons, quantify the ground-truth uncertainty or its influence on the RMSEs, and state whether the conclusions change when the suspected Aeva ground-truth error is removed.
[Section IV] The evaluation compares only against CROON and covers ten scenes per dataset, with some CROON failures excluded from the plots ("not visible in the plot"). The abstract's claim of being "on par or better in calibration accuracy than existing approaches" cannot be supported by a single baseline and by plots that omit failures. Please include at least one additional recent open-source baseline, report all runs including failures, and provide per-scene and per-LiDAR numeric results rather than only aggregated RMSEs.

minor comments (4)

[Section III.A] The sentence "An iterative process was used to optimize both the voxel size and the radius size" gives no details on the objective, the search range, or the stopping criterion; please provide this information or point to the exact configuration in the repository.
[Algorithm 1] The pseudocode is ambiguous about the recomputation step: "Remove all calibrations involving the source LiDAR" is immediately followed by "for each calibration in calibrations tmp involving the last calibrated source," and the conflict between these two operations needs clarification.
[Section IV] The results are presented only through plots of aggregated RMSEs; numeric tables with per-axis, per-LiDAR values, and confidence intervals would make the comparison reproducible and easier for readers to assess.
[Section II.A] The evaluation of existing open-source methods is described briefly and relies on the authors' ability to compile and run them; a short table summarizing which methods were attempted and why they were excluded would be helpful context for the choice of CROON as the only baseline.

Circularity Check

2 steps flagged · score 6.0 of 10

The validation is partly circular: the parameter set (including the 0.2 fitness gate) was optimized on the same EDGAR and HeLiPR scenes that later appear as the reported accuracy results, making the headline accuracy an in-sample fit rather than an out-of-sample prediction.

fitted input called prediction [Section III.D and Section IV.A/B]
"For the used methods, e.g., RANSAC, FPFH, TEASER++, and GICP, parameters were examined to determine approximately the optimal values for the calibration process. The priority was on a robust calibration with the best possible accuracy, giving the runtime a lower priority. We computed the variance and the mean average error (MAE) of translational and rotational components and determined the single parameter set used for all calibrations across all configurations and datasets used in this work. ..."

Section III.D states that the parameters were chosen by minimizing MAE on 'all configurations and datasets used in this work' — the same EDGAR and HeLiPR scenes that Section IV uses as the evaluation benchmark. Section III.A further says an 'iterative process was used to optimize both the voxel size and the radius size.' Therefore, the RMSE values reported in Section IV are the training objective evaluated on the training scenes, not an independent test of the method. The abstract's 'better generalized' claim is not supported by a held-out evaluation; the reported accuracy is partly forced by the parameter selection.
other [Section III.B and Algorithm 1]
"For every successful calibration, a source point cloud is transformed into the target point cloud and merged, building a larger cloud. The success of an alignment is measured by the fitness score. We found a score of greater 0.2 sufficient."

The 0.2 fitness threshold is the single gate that decides whether a transform is accepted, whether the source cloud is merged, and whether the loop continues or terminates in Algorithm 1. The paper states that this value was found by testing ('We found a score of greater 0.2 sufficient'), and the parameter-optimization section indicates that thresholds were examined on the same EDGAR/HeLiPR data used later for evaluation. No distribution of fitness scores for correct vs. incorrect alignments or sensitivity analysis is provided, so the reported successful cascade on those datasets is an in-sample outcome of a threshold tuned on them rather than an independently validated success criterion.

full rationale

The transformation pipeline itself is not circular by construction: coarse alignment computes FPFH features and TEASER++ transforms from the point clouds, fine registration uses Open3D GICP, and the iterative merging is a deterministic heuristic whose inputs are point clouds, not the target extrinsic parameters. No load-bearing uniqueness theorem or self-citation supplies the result; the cited prior work (CROON, FPFH, TEASER++, GICP, Open3D, HeLiPR) is external, and the only self-citations (EDGAR [9], GMMCalib [15]) are background references, not argumentative support. However, the empirical validation is partially circular: Section III.D optimizes the method's parameters using the same EDGAR and HeLiPR datasets whose accuracy is then reported in Section IV. The fitness threshold used in Algorithm 1 as the acceptance/termination gate is part of that tuned parameter set ('We found a score of greater 0.2 sufficient'). Thus the headline claim of 'on par or better in calibration accuracy' rests on in-sample accuracy numbers rather than out-of-sample predictions. This prevents the paper from being assigned a low score, but the core method is an independent algorithmic pipeline, so a score of 6 reflecting partial, evaluation-level circularity is appropriate.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on several fitted parameters (voxel size, FPFH radius, fitness threshold, and a larger parameter set held in the repository) and on explicit domain assumptions about overlap connectivity, static scenes, and ground truth quality. No new physical or conceptual entities are introduced; the merged point cloud is a data structure, not a new object.

free parameters (4)

voxel_size = 0.35 m
Coarse alignment voxel size; the authors state it was optimized iteratively along with the radius.
fpfh_radius = 1.75 m (five times voxel size)
Neighbor radius for FPFH feature computation, coupled to the chosen voxel size.
fitness_threshold = 0.2
GICP fitness score above this value is treated as a successful alignment; chosen by testing on the evaluation data.
ransac_fpfh_teaser_gicp_parameters = Not listed in paper (deferred to GitHub)
A single parameter set was determined by minimizing variance and MAE over the same datasets used for evaluation.

assumptions (5)

standard math TEASER++, FPFH, and GICP implementations behave as documented and provide the expected registration solutions.
The method relies on these off-the-shelf algorithms, cited from the literature.
domain assumption The FOV overlap graph contains a connected path to the target LiDAR.
Stated as a critical assumption in Section III.B; without it at least one LiDAR cannot be calibrated.
domain assumption The scene is static and point clouds are undistorted.
EDGAR data were recorded statically and HeLiPR clouds were corrected with the provided toolbox; moving objects or distortion would break matching.
ad hoc to paper A GICP fitness score above 0.2 implies a correct alignment.
Threshold chosen by the authors without theoretical or statistical justification; wrong local minima with high fitness could corrupt the merged cloud.
domain assumption The manual EDGAR ground truth is accurate.
Reported EDGAR errors are computed against a manual cube-based alignment whose uncertainty is not quantified.

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Cite this review

Pith. "Pith review of Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework." pith.science (2026). https://pith.science/paper/VRZWFEJS

@misc{pith2026250111088,
  author       = {Pith},
  title        = {Pith review of: Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VRZWFEJS}},
  note         = {Machine review of arXiv:2501.11088}
}

read the original abstract

Today's autonomous vehicles rely on a multitude of sensors to perceive their environment. To improve the perception or create redundancy, the sensor's alignment relative to each other must be known. With Multi-LiCa, we present a novel approach for the alignment, e.g. calibration. We present an automatic motion- and targetless approach for the extrinsic multi LiDAR-to-LiDAR calibration without the need for additional sensor modalities or an initial transformation input. We propose a two-step process with feature-based matching for the coarse alignment and a GICP-based fine registration in combination with a cost-based matching strategy. Our approach can be applied to any number of sensors and positions if there is a partial overlap between the field of view of single sensors. We show that our pipeline is better generalized to different sensor setups and scenarios and is on par or better in calibration accuracy than existing approaches. The presented framework is integrated in ROS 2 but can also be used as a standalone application. To build upon our work, our source code is available at: https://github.com/TUMFTM/Multi_LiCa.

Figures

Figures reproduced from arXiv: 2501.11088 by the authors.

**Figure 2.** FOV of EDGAR LiDAR sensors. on the vehicle’s roof [5]–[8]. In contrast to the trend, the Technical University of Munich (TUM) research vehicle for autonomous driving, EDGAR (Excellent Driving Garching), is equipped with an angled multi-LiDAR setup distributed around the roof frame [9]. The distinctive LiDAR setup includes two rotating midrange Ouster OS-1 and two solid-state long-range Seyond (former Innovusion) Fa… view at source ↗

**Figure 3.** EDGAR front (blue), left (green), and right (orange) [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: Samples of the HeLiPR dataset with LiDAR point clouds of Ouster (blue), Velodyne (black), Livox (orange) and [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

**Figure 6.** Figure 6: Samples of the EDGAR dataset with front (blue), rear (orange), left (green) and right (black) LiDAR point clouds. [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

**Figure 7.** Figure 7: Translational and rotational error of Multi-LiCa and CROON on the HeLiPR and EDGAR dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]

**Figure 8.** Figure 8: Misalignment of HeLiPR Aeva (green) and Ouster [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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