# IDOL: A Framework for IMU-DVS Odometry using Lines

arXiv: https://arxiv.org/abs/2008.05749 · HTML: https://mlsanity.com/paper/2008.05749/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 27 of 27)

- [Deep Event Visual Odometry](https://mlsanity.com/paper/2312.09800/) (2023)
- [Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera](https://mlsanity.com/paper/2408.03225/) (2024)
- [Comparing Representations in Tracking for Event Camera-based SLAM](https://mlsanity.com/paper/2104.09887/) (2021)
- [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)
- [A 5-Point Minimal Solver for Event Camera Relative Motion Estimation](https://mlsanity.com/paper/2309.17054/) (2023)
- [Continuous Event-Line Constraint for Closed-Form Velocity Initialization](https://mlsanity.com/paper/2109.04313/) (2021)
- [An N-Point Linear Solver for Line and Motion Estimation with Event Cameras](https://mlsanity.com/paper/2404.00842/) (2024)
- [Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers](https://mlsanity.com/paper/2503.03307/) (2025)
- [Asynchronous Optimisation for Event-based Visual Odometry](https://mlsanity.com/paper/2203.01037/) (2022)
- [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)
- [Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping](https://mlsanity.com/paper/2102.08145/) (2021)
- [Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression](https://mlsanity.com/paper/2306.01188/) (2023)
- [Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation](https://mlsanity.com/paper/2401.09296/) (2024)
- [Event Camera Meets Mobile Embodied Perception: Abstraction, Algorithm, Acceleration, Application](https://mlsanity.com/paper/2503.22943/) (2025)
- [EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization](https://mlsanity.com/paper/2408.01370/) (2024)
- [Continuous-Time Gaussian Process Motion-Compensation for Event-vision Pattern Tracking with Distance Fields](https://mlsanity.com/paper/2303.02672/) (2023)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)
- [Ego-motion Estimation Based on Fusion of Images and Events](https://mlsanity.com/paper/2207.05588/) (2022)
- [AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression](https://mlsanity.com/paper/2411.12175/) (2024)
- [Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera](https://mlsanity.com/paper/2409.15581/) (2024)
- [IONext: Unlocking the Next Era of Inertial Odometry](https://mlsanity.com/paper/2507.17089/) (2025)
- [Odometry Without Correspondence from Inertially Constrained Ruled Surfaces](https://mlsanity.com/paper/2512.00327/) (2025)
- [LECalib: Line-Based Event Camera Calibration](https://mlsanity.com/paper/2512.22441/) (2025)
- [RoEL: Robust Event-based 3D Line Reconstruction](https://mlsanity.com/paper/2602.18258/) (2026)
- [A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras](https://mlsanity.com/paper/2606.09139/) (2026)

## Abstract (arXiv)

In this paper, we introduce IDOL, an optimization-based framework for IMU-DVS Odometry using Lines. Event cameras, also called Dynamic Vision Sensors (DVSs), generate highly asynchronous streams of events triggered upon illumination changes for each individual pixel. This novel paradigm presents advantages in low illumination conditions and high-speed motions. Nonetheless, this unconventional sensing modality brings new challenges to perform scene reconstruction or motion estimation. The proposed method offers to leverage a continuous-time representation of the inertial readings to associate each event with timely accurate inertial data. The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space. A novel attraction/repulsion mechanism is presented to accurately estimate the lines' extremities, avoiding their explicit detection in the event data. The proposed method is benchmarked against a state-of-the-art frame-based visual-inertial odometry framework using public datasets. The results show that IDOL performs at the same order of magnitude on most datasets and even shows better orientation estimates. These findings can have a great impact on new algorithms for DVS.