# Exploring Event Camera-based Odometry for Planetary Robots

arXiv: https://arxiv.org/abs/2204.05880 · HTML: https://mlsanity.com/paper/2204.05880/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [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)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Asynchronous, Photometric Feature Tracking using Events and Frames](https://mlsanity.com/paper/1807.09713/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [Event-based, 6-DOF Camera Tracking from Photometric Depth Maps](https://mlsanity.com/paper/1607.03468/) (2016)
- [Autonomous Quadrotor Flight despite Rotor Failure with Onboard Vision Sensors: Frames vs. Events](https://mlsanity.com/paper/2102.13406/) (2021)
- [Continuous-Time vs. Discrete-Time Vision-based SLAM: A Comparative Study](https://mlsanity.com/paper/2202.08894/) (2022)
- [Range-Visual-Inertial Odometry: Scale Observability Without Excitation](https://mlsanity.com/paper/2103.15215/) (2021)
- [MIMC-VINS: A Versatile and Resilient Multi-IMU Multi-Camera Visual-Inertial Navigation System](https://mlsanity.com/paper/2006.15699/) (2020)
- [xVIO: A Range-Visual-Inertial Odometry Framework](https://mlsanity.com/paper/2010.06677/) (2020)

## Built on by (most cited first, 30 of 34)

- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [Deep Event Visual Odometry](https://mlsanity.com/paper/2312.09800/) (2023)
- [V2CE: Video to Continuous Events Simulator](https://mlsanity.com/paper/2309.08891/) (2023)
- [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)
- [Event-based Simultaneous Localization and Mapping: A Comprehensive Survey](https://mlsanity.com/paper/2304.09793/) (2023)
- [Deep Visual Odometry with Events and Frames](https://mlsanity.com/paper/2309.09947/) (2023)
- [E-Motion: Future Motion Simulation via Event Sequence Diffusion](https://mlsanity.com/paper/2410.08649/) (2024)
- [Elite-EvGS: Learning Event-based 3D Gaussian Splatting by Distilling Event-to-Video Priors](https://mlsanity.com/paper/2409.13392/) (2024)
- [A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions](https://mlsanity.com/paper/2206.11541/) (2022)
- [E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras](https://mlsanity.com/paper/2306.09078/) (2023)
- [EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping](https://mlsanity.com/paper/2312.11911/) (2023)
- [Cross-Modal Semi-Dense 6-DoF Tracking of an Event Camera in Challenging Conditions](https://mlsanity.com/paper/2401.08043/) (2024)
- [Neuromorphic Facial Analysis with Cross-Modal Supervision](https://mlsanity.com/paper/2409.10213/) (2024)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [Neuromorphic Valence and Arousal Estimation](https://mlsanity.com/paper/2401.16058/) (2024)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [Density Invariant Contrast Maximization for Neuromorphic Earth Observations](https://mlsanity.com/paper/2304.14125/) (2023)
- [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024)
- [Hardware, Algorithms, and Applications of the Neuromorphic Vision Sensor: a Review](https://mlsanity.com/paper/2504.08588/) (2025)
- [Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields](https://mlsanity.com/paper/2303.02672/) (2023)
- [Event-based Sensor Fusion and Application on Odometry: A Survey](https://mlsanity.com/paper/2410.15480/) (2024)
- [Spatio-temporal Transformers for Action Unit Classification with Event Cameras](https://mlsanity.com/paper/2410.21958/) (2024)
- [Asynchronous Multi-Object Tracking with an Event Camera](https://mlsanity.com/paper/2505.08126/) (2025)
- [Multi-Timescale Motion-Decoupled Spiking Transformer for Audio-Visual Zero-Shot Learning](https://mlsanity.com/paper/2505.19938/) (2025)
- [Monocular Event-Inertial Odometry with Adaptive decay-based Time Surface and Polarity-aware Tracking](https://mlsanity.com/paper/2409.13971/) (2024)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception](https://mlsanity.com/paper/2504.16616/) (2025)
- [Learning Rock Pushability on Rough Planetary Terrain](https://mlsanity.com/paper/2505.09833/) (2025)
- [E-CHUM: Event-based Cameras for Human Detection and Urban Monitoring](https://mlsanity.com/paper/2512.11076/) (2025)

## Abstract (arXiv)

Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions. However, existing event-based visual-inertial odometry (VIO) algorithms either suffer from high tracking errors or are brittle, since they cannot cope with significant depth uncertainties caused by an unforeseen loss of tracking or other effects. In this work, we introduce EKLT-VIO, which addresses both limitations by combining a state-of-the-art event-based frontend with a filter-based backend. This makes it both accurate and robust to uncertainties, outperforming event- and frame-based VIO algorithms on challenging benchmarks by 32%. In addition, we demonstrate accurate performance in hover-like conditions (outperforming existing event-based methods) as well as high robustness in newly collected Mars-like and high-dynamic-range sequences, where existing frame-based methods fail. In doing so, we show that event-based VIO is the way forward for vision-based exploration on Mars.