# ECTLO: Effective Continuous-time Odometry Using Range Image for LiDAR with Small FoV

arXiv: https://arxiv.org/abs/2206.08517 · HTML: https://mlsanity.com/paper/2206.08517/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 17 of 17)

- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package](https://mlsanity.com/paper/2109.07982/) (2021)
- [FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization](https://mlsanity.com/paper/1811.10136/) (2018)
- [IMLS-SLAM: scan-to-model matching based on 3D data](https://mlsanity.com/paper/1802.08633/) (2018)
- [CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure](https://mlsanity.com/paper/2109.12979/) (2021)
- [Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV](https://mlsanity.com/paper/1909.06700/) (2019)
- [BALM: Bundle Adjustment for Lidar Mapping](https://mlsanity.com/paper/2010.08215/) (2020)
- [Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping](https://mlsanity.com/paper/1810.06802/) (2018)
- [The Hilti SLAM Challenge Dataset](https://mlsanity.com/paper/2109.11316/) (2021)
- [Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM](https://mlsanity.com/paper/1711.01691/) (2017)
- [Towards High-Performance Solid-State-LiDAR-Inertial Odometry and Mapping](https://mlsanity.com/paper/2010.13150/) (2020)
- [LOCUS 2.0: Robust and Computationally Efficient Lidar Odometry for Real-Time Underground 3D Mapping](https://mlsanity.com/paper/2205.11784/) (2022)
- [Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning](https://mlsanity.com/paper/2006.11034/) (2020)
- [Efficient LiDAR Odometry for Autonomous Driving](https://mlsanity.com/paper/2104.10879/) (2021)
- [UPSLAM: Union of Panoramas SLAM](https://mlsanity.com/paper/2101.00585/) (2021)
- [LLOL: Low-Latency Odometry for Spinning Lidars](https://mlsanity.com/paper/2110.01725/) (2021)

## Built on by (most cited first, 2 of 2)

- [Traj-LO: In Defense of LiDAR-Only Odometry Using an Effective Continuous-Time Trajectory](https://mlsanity.com/paper/2309.13842/) (2023)
- [Traj-LIO: A Resilient Multi-LiDAR Multi-IMU State Estimator Through Sparse Gaussian Process](https://mlsanity.com/paper/2402.09189/) (2024)

## Abstract (arXiv)

Prism-based LiDARs are more compact and cheaper than the conventional mechanical multi-line spinning LiDARs, which have become increasingly popular in robotics, recently. However, there are several challenges for these new LiDAR sensors, including small field of view, severe motion distortions, and irregular patterns, which hinder them from being widely used in LiDAR odometry, practically. To tackle these problems, we present an effective continuous-time LiDAR odometry (ECTLO) method for the Risley-prism-based LiDARs with non-repetitive scanning patterns. A single range image covering historical points in LiDAR's small FoV is adopted for efficient map representation. To account for the noisy data from occlusions after map updating, a filter-based point-to-plane Gaussian Mixture Model is used for robust registration. Moreover, a LiDAR-only continuous-time motion model is employed to relieve the inevitable distortions. Extensive experiments have been conducted on various testbeds using the prism-based LiDARs with different scanning patterns, whose promising results demonstrate the efficacy of our proposed approach.