# Event-based Sensor Fusion and Application on Odometry: A Survey

arXiv: https://arxiv.org/abs/2410.15480 · HTML: https://mlsanity.com/paper/2410.15480/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 30 of 30)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping](https://mlsanity.com/paper/1910.02490/) (2019)
- [Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs](https://mlsanity.com/paper/2101.06894/) (2021)
- [Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera](https://mlsanity.com/paper/1811.10180/) (2018)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [FAST-LIO: A Fast, Robust LiDAR-inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter](https://mlsanity.com/paper/2010.08196/) (2020)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Semi-Dense 3D Reconstruction with a Stereo Event Camera](https://mlsanity.com/paper/1807.07429/) (2018)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [Exploring Event Camera-based Odometry for Planetary Robots](https://mlsanity.com/paper/2204.05880/) (2022)
- [ESVIO: Event-based Stereo Visual Inertial Odometry](https://mlsanity.com/paper/2212.13184/) (2022)
- [EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms](https://mlsanity.com/paper/2205.03467/) (2022)
- [Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception](https://mlsanity.com/paper/2110.04988/) (2021)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras](https://mlsanity.com/paper/2207.01009/) (2022)
- [Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow](https://mlsanity.com/paper/2403.07432/) (2024)
- [Visual-Inertial Odometry of Aerial Robots](https://mlsanity.com/paper/1906.03289/) (2019)
- [Feature-based Event Stereo Visual Odometry](https://mlsanity.com/paper/2107.04921/) (2021)
- [ECMD: An Event-Centric Multisensory Driving Dataset for SLAM](https://mlsanity.com/paper/2311.02327/) (2023)
- [IMU-Aided Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2405.04071/) (2024)
- [CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving](https://mlsanity.com/paper/2408.08500/) (2024)
- [A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments](https://mlsanity.com/paper/2210.00812/) (2022)
- [COIN-LIO: Complementary Intensity-Augmented LiDAR Inertial Odometry](https://mlsanity.com/paper/2310.01235/) (2023)
- [Stereo Event-based Visual-Inertial Odometry](https://mlsanity.com/paper/2303.05086/) (2023)
- [Analysis of Deep Learning-Based Colorization and Super-Resolution Techniques for Lidar Imagery](https://mlsanity.com/paper/2409.11532/) (2024)

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

- [E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring](https://mlsanity.com/paper/2512.11076/) (2025)
- [Unified Complex-valued Neural Network: A Magnitude-Phase Computational Model for Event-Driven Neuromorphic Learning](https://mlsanity.com/paper/2606.29099/) (2026)

## Abstract (arXiv)

Event cameras, inspired by biological vision, are asynchronous sensors that detect changes in brightness, offering notable advantages in environments characterized by high-speed motion, low lighting, or wide dynamic range. These distinctive properties render event cameras particularly effective for sensor fusion in robotics and computer vision, especially in enhancing traditional visual or LiDAR-inertial odometry. Conventional frame-based cameras suffer from limitations such as motion blur and drift, which can be mitigated by the continuous, low-latency data provided by event cameras. Similarly, LiDAR-based odometry encounters challenges related to the loss of geometric information in environments such as corridors. To address these limitations, unlike the existing event camera-related surveys, this paper presents a comprehensive overview of recent advancements in event-based sensor fusion for odometry applications particularly, investigating fusion strategies that incorporate frame-based cameras, inertial measurement units (IMUs), and LiDAR. The survey critically assesses the contributions of these fusion methods to improving odometry performance in complex environments, while highlighting key applications, and discussing the strengths, limitations, and unresolved challenges. Additionally, it offers insights into potential future research directions to advance event-based sensor fusion for next-generation odometry applications.