# Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface

arXiv: https://arxiv.org/abs/2305.08962 · HTML: https://mlsanity.com/paper/2305.08962/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 15 May 2023.

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

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios](https://mlsanity.com/paper/1709.06310/) (2017)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Event-aided Direct Sparse Odometry](https://mlsanity.com/paper/2204.07640/) (2022)
- [Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control](https://mlsanity.com/paper/1909.06586/) (2019)
- [Speed Invariant Time Surface for Learning to Detect Corner Points with Event-Based Cameras](https://mlsanity.com/paper/1903.11332/) (2019)
- [A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors](https://mlsanity.com/paper/1901.03638/) (2019)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (2022)
- [ESVIO: Event-based Stereo Visual Inertial Odometry](https://mlsanity.com/paper/2212.13184/) (2022)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)

## Built on by (most cited first, 5 of 5)

- [EF-Calib: Spatiotemporal Calibration of Event- and Frame-Based Cameras Using Continuous-Time Trajectories](https://mlsanity.com/paper/2405.17278/) (2024)
- [Fast Feature Field (F3): A Predictive Representation of Events](https://mlsanity.com/paper/2509.25146/) (2025)
- [EREBUS: End-to-end Robust Event Based Underwater Simulation](https://mlsanity.com/paper/2511.01381/) (2025)
- [EROAM: Event-based Camera Rotational Odometry and Mapping in Real-time](https://mlsanity.com/paper/2411.11004/) (2024)
- [Radar and Event Camera Fusion for Agile Robot Ego-Motion Estimation](https://mlsanity.com/paper/2506.18443/) (2025)

## Abstract (arXiv)

Our paper proposes a direct sparse visual odometry method that combines event and RGB-D data to estimate the pose of agile-legged robots during dynamic locomotion and acrobatic behaviors. Event cameras offer high temporal resolution and dynamic range, which can eliminate the issue of blurred RGB images during fast movements. This unique strength holds a potential for accurate pose estimation of agile-legged robots, which has been a challenging problem to tackle. Our framework leverages the benefits of both RGB-D and event cameras to achieve robust and accurate pose estimation, even during dynamic maneuvers such as jumping and landing a quadruped robot, the Mini-Cheetah. Our major contributions are threefold: Firstly, we introduce an adaptive time surface (ATS) method that addresses the whiteout and blackout issue in conventional time surfaces by formulating pixel-wise decay rates based on scene complexity and motion speed. Secondly, we develop an effective pixel selection method that directly samples from event data and applies sample filtering through ATS, enabling us to pick pixels on distinct features. Lastly, we propose a nonlinear pose optimization formula that simultaneously performs 3D-2D alignment on both RGB-based and event-based maps and images, allowing the algorithm to fully exploit the benefits of both data streams. We extensively evaluate the performance of our framework on both public datasets and our own quadruped robot dataset, demonstrating its effectiveness in accurately estimating the pose of agile robots during dynamic movements.