# ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching

arXiv: https://arxiv.org/abs/2306.08690 · HTML: https://mlsanity.com/paper/2306.08690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [LO-Net: Deep Real-time Lidar Odometry](https://mlsanity.com/paper/1904.08242/) (2019)
- [Fast and Robust Registration of Partially Overlapping Point Clouds](https://mlsanity.com/paper/2112.09922/) (2021)
- [DMLO: Deep Matching LiDAR Odometry](https://mlsanity.com/paper/2004.03796/) (2020)
- [PixSet : An Opportunity for 3D Computer Vision to Go Beyond Point Clouds With a Full-Waveform LiDAR Dataset](https://mlsanity.com/paper/2102.12010/) (2021)
- [X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments](https://mlsanity.com/paper/2211.16335/) (2022)
- [A New Approach to 3D ICP Covariance Estimation](https://mlsanity.com/paper/1909.05722/) (2019)
- [Resolving Multi-path Interference in Time-of-Flight Imaging via Modulation Frequency Diversity and Sparse Regularization](https://mlsanity.com/paper/1404.1116/) (2014)
- [Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving](https://mlsanity.com/paper/2207.14674/) (2022)
- [Robust GPS-Vision Localization via Integrity-Driven Landmark Attention](https://mlsanity.com/paper/2101.04836/) (2021)
- [Mitigating Shadows in Lidar Scan Matching using Spherical Voxels](https://mlsanity.com/paper/2208.01150/) (2022)
- [Performance Analysis of NDT-based Graph SLAM for Autonomous Vehicle in Diverse Typical Driving Scenarios of Hong Kong](https://mlsanity.com/paper/1810.04848/) (2018)

## Built on by (most cited first, 1 of 1)

- [Characterizing Lidar Range-Measurement Ambiguity due to Multiple Returns](https://mlsanity.com/paper/2604.09282/) (2026)

## Abstract (arXiv)

Distribution-to-Distribution (D2D) point cloud registration algorithms are fast, interpretable, and perform well in unstructured environments. Unfortunately, existing strategies for predicting solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper we introduce the Iterative Closest Ellipsoidal Transform (ICET), a novel 3D LIDAR scan-matching algorithm that re-envisions NDT in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a LIDAR scan into voxels in order to analyze complex scenes by considering many smaller local point distributions, however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction into computing a localization solution and predicting the solution-error covariance. In order to demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three LIDAR tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. This level of accuracy, combined with the fact that the algorithm is fully interpretable, make it well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET