# ES-PTAM: Event-based Stereo Parallel Tracking and Mapping

arXiv: https://arxiv.org/abs/2408.15605 · HTML: https://mlsanity.com/paper/2408.15605/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 23 of 23)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age](https://mlsanity.com/paper/1606.05830/) (2016)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [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)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (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)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [Semi-Dense 3D Reconstruction with a Stereo Event Camera](https://mlsanity.com/paper/1807.07429/) (2018)
- [Event-based Motion Segmentation with Spatio-Temporal Graph Cuts](https://mlsanity.com/paper/2012.08730/) (2020)
- [TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset](https://mlsanity.com/paper/2108.07329/) (2021)
- [DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions](https://mlsanity.com/paper/2202.02556/) (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)
- [CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization](https://mlsanity.com/paper/2403.08119/) (2024)
- [Deep Event Visual Odometry](https://mlsanity.com/paper/2312.09800/) (2023)
- [Realtime Time Synchronized Event-based Stereo](https://mlsanity.com/paper/1803.09025/) (2018)
- [Multi-Event-Camera Depth Estimation and Outlier Rejection by Refocused Events Fusion](https://mlsanity.com/paper/2207.10494/) (2022)
- [Feature-based Event Stereo Visual Odometry](https://mlsanity.com/paper/2107.04921/) (2021)
- [IMU-Aided Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2405.04071/) (2024)
- [Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression](https://mlsanity.com/paper/2306.01188/) (2023)

## Built on by (most cited first, 7 of 7)

- [Event-based Stereo Depth Estimation: A Survey](https://mlsanity.com/paper/2409.17680/) (2024)
- [ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras](https://mlsanity.com/paper/2410.09374/) (2024)
- [SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM](https://mlsanity.com/paper/2504.00139/) (2025)
- [Simultaneous Motion And Noise Estimation with Event Cameras](https://mlsanity.com/paper/2504.04029/) (2025)
- [A Survey of 3D Reconstruction with Event Cameras](https://mlsanity.com/paper/2505.08438/) (2025)
- [DERD-Net: Learning Depth from Event-based Ray Densities](https://mlsanity.com/paper/2504.15863/) (2025)
- [E-S2Feat:Semantic-Guided Spiking Local Feature Detection and Description for Event Cameras](https://mlsanity.com/paper/2608.14027/) (2026)

## Abstract (arXiv)

Visual Odometry (VO) and SLAM are fundamental components for spatial perception in mobile robots. Despite enormous progress in the field, current VO/SLAM systems are limited by their sensors' capability. Event cameras are novel visual sensors that offer advantages to overcome the limitations of standard cameras, enabling robots to expand their operating range to challenging scenarios, such as high-speed motion and high dynamic range illumination. We propose a novel event-based stereo VO system by combining two ideas: a correspondence-free mapping module that estimates depth by maximizing ray density fusion and a tracking module that estimates camera poses by maximizing edge-map alignment. We evaluate the system comprehensively on five real-world datasets, spanning a variety of camera types (manufacturers and spatial resolutions) and scenarios (driving, flying drone, hand-held, egocentric, etc). The quantitative and qualitative results demonstrate that our method outperforms the state of the art in majority of the test sequences by a margin, e.g., trajectory error reduction of 45% on RPG dataset, 61% on DSEC dataset, and 21% on TUM-VIE dataset. To benefit the community and foster research on event-based perception systems, we release the source code and results: https://github.com/tub-rip/ES-PTAM