# Characterizing Lidar Range-Measurement Ambiguity due to Multiple Returns

arXiv: https://arxiv.org/abs/2604.09282 · HTML: https://mlsanity.com/paper/2604.09282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 7 of 7)

- [Lidar for Autonomous Driving: The principles, challenges, and trends for automotive lidar and perception systems](https://mlsanity.com/paper/2004.08467/) (2020)
- [The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth](https://mlsanity.com/paper/2003.05691/) (2020)
- [You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks](https://mlsanity.com/paper/2210.09482/) (2022)
- [Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving](https://mlsanity.com/paper/2207.14674/) (2022)
- [Multipath Effects on Frequency-Locked Loops (FLLs) and FLL-derived Doppler Measurements](https://mlsanity.com/paper/2303.02798/) (2023)
- [ICET Online Accuracy Characterization for Geometry-Based Laser Scan Matching](https://mlsanity.com/paper/2306.08690/) (2023)
- [A Probabilistic Formulation of LiDAR Mapping with Neural Radiance Fields](https://mlsanity.com/paper/2411.01725/) (2024)

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

- [TriO: Tri-Modal Unsupervised Occupancy World Model for Anything Perception](https://mlsanity.com/paper/2609.32013/) (2026)

## Abstract (arXiv)

Reliable position and attitude sensing is critical for highly automated vehicles that operate on conventional roadways. Lidar sensors are increasingly incorporated into pose-estimation systems. Despite its great utility, lidar is a complex sensor, and its performance in roadway environments is not yet well understood. For instance, it is often assumed in lidar-localization algorithms that a lidar will always identify a unique surface along a given raypath. However, this assumption is not always true, as ample prior evidence exists to suggest that lidar units may generate measurements probabilistically when more than one scattering surface appears within the lidar's conical beam. In this paper, we analyze lidar datasets to characterize cases with probabilistic returns along particular raypaths. Our contribution is to present representative cumulative distribution functions (CDFs) for raypaths observed by two different mechanically rotating lidar units with stationary bases. In subsequent discussion, we outline a qualitative methodology to assess the effect of probabilistic multi-return cases on lidar-based localization.