Key Concepts
- Event cameras (DVS) — Asynchronous per-pixel brightness change detection, μs temporal resolution
- Advantages — HDR (140dB+), no motion blur, low latency, low power
- Challenges — No absolute intensity, sparse asynchronous output, requires new algorithms
- Event representations — Event frames, time surfaces, voxel grids, spike tensors
Foundations
| Resource | Author/Year | Key Concepts |
|---|---|---|
| Event-based Vision Survey | Gallego 2020 | Comprehensive survey of event camera algorithms |
| Awesome-Event-based-SLAM | KwanWaiPang | Curated GitHub list of event-based SLAM papers |
Systems
| System | Author/Year | Key Concepts |
|---|---|---|
| EVO | Rebecq 2017 | Event-based Visual Odometry, 3D reconstruction from events |
| ESVO | Zhou 2021 | Event-based Stereo Visual Odometry |
| Ultimate-SLAM | Vidal 2018 | Events + frames + IMU fusion |
| EKLT | Gehrig 2020 | Event-based KLT feature tracking |
| ESVIO | Chen 2023 | Event-based Stereo VIO |
| EDS | Hidalgo-Carrió 2022 | Event-aided direct sparse odometry |
| DEVO | Klenk 2024 | Deep event-based visual odometry, DPVO-style patch-based, trained on simulated events |
| VIO-GO | Sakhrieh 2025 | Event-based VIO with optimized parameters for HDR scenarios |