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End-to-End LiDAR optimization for 3D point cloud registration

Siddhant Katyan, Marc-André Gardner, Jean-François Lalonde

TL;DR

The paper tackles the sensitivity of 3D point cloud registration to both LiDAR sensing and registration hyperparameters by proposing an end-to-end adaptive framework that jointly optimizes these parameters using CMA-ES. It defines a LiDAR function and a registration function, links them through a joint objective that minimizes the transform error across pose pairs, and evaluates the approach in CARLA-based simulations across diverse environments. Results demonstrate that joint optimization substantially improves registration recall and reduces pose errors, with notable gains for global registration methods, and show that the optimized parameters generalize to similar scenes beyond the training set. The work highlights the practical impact of task-aware sensing, offering a path toward more robust autonomous perception pipelines and underscoring the need for realistic simulation to bridge sim-to-real gaps.

Abstract

LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR configurations, leading to suboptimal data collection and significant computational overhead for sampling, noise filtering, and parameter tuning. In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters, jointly optimizing LiDAR acquisition and registration hyperparameters. By integrating registration feedback into the sensing loop, our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency. Evaluations in the CARLA simulation demonstrate that our method outperforms fixed-parameter baselines while retaining generalization abilities, highlighting the potential of adaptive LiDAR for autonomous perception and robotic applications.

End-to-End LiDAR optimization for 3D point cloud registration

TL;DR

The paper tackles the sensitivity of 3D point cloud registration to both LiDAR sensing and registration hyperparameters by proposing an end-to-end adaptive framework that jointly optimizes these parameters using CMA-ES. It defines a LiDAR function and a registration function, links them through a joint objective that minimizes the transform error across pose pairs, and evaluates the approach in CARLA-based simulations across diverse environments. Results demonstrate that joint optimization substantially improves registration recall and reduces pose errors, with notable gains for global registration methods, and show that the optimized parameters generalize to similar scenes beyond the training set. The work highlights the practical impact of task-aware sensing, offering a path toward more robust autonomous perception pipelines and underscoring the need for realistic simulation to bridge sim-to-real gaps.

Abstract

LiDAR sensors are a key modality for 3D perception, yet they are typically designed independently of downstream tasks such as point cloud registration. Conventional registration operates on pre-acquired datasets with fixed LiDAR configurations, leading to suboptimal data collection and significant computational overhead for sampling, noise filtering, and parameter tuning. In this work, we propose an adaptive LiDAR sensing framework that dynamically adjusts sensor parameters, jointly optimizing LiDAR acquisition and registration hyperparameters. By integrating registration feedback into the sensing loop, our approach optimally balances point density, noise, and sparsity, improving registration accuracy and efficiency. Evaluations in the CARLA simulation demonstrate that our method outperforms fixed-parameter baselines while retaining generalization abilities, highlighting the potential of adaptive LiDAR for autonomous perception and robotic applications.
Paper Structure (16 sections, 7 equations, 5 figures, 1 table)

This paper contains 16 sections, 7 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. LiDAR and registration hyperparameter optimization
  4. Point cloud registration
  5. Optimization framework
  6. Optimization objective
  7. Defining the LiDAR function
  8. Defining the registration function
  9. Optimization procedure
  10. Experimental validation
  11. Data collection
  12. Training details
  13. Evaluation methodology
  14. Experimental results
  15. Parameter analysis
  16. ...and 1 more sections

Figures (5)

  • Figure 1: Variations in LiDAR scanning parameters and registration algorithm settings lead to divergent registration quality, demonstrating the sensitivity of the pipeline to manual tuning and the need for robust and adaptive end-to-end parameter optimization. Positive and negative interactions among parameters are highlighted in red and blue respectively.
  • Figure 2: LiDAR optimization framework for point cloud registration optimization. $\mathbf{T}_\text{gt}$ and $\mathbf{T}_\text{est}$ are ground-truth matrix and estimated transform respectively for point cloud registration.
  • Figure 3: Error distribution on the test data including failure registration cases for FGR structured environment.
  • Figure 4: Qualitative results of point cloud registration on point clouds of example test scenes, under different optimization schemes. From left to right: optimizing the LiDAR only, optimizing the registration hyperparameters only, performing joint optimization. Only the relevant parts are shown for visualization purposes.
  • Figure 5: Jointly optimized LiDAR and FGR parameters vary across different scene types: unstructured, semi-structured, and structured.