Work

Extended Kalman Filter-Based Visual Odometry in Dynamic Environments Using Modified 1-Point RANSAC

Year: 2025

Type: article

Abstract: Visual odometry in dynamic environments is particularly challenging, as moving objects often cause incorrect data associations and large pose estimation errors. Traditional EKF-based VO methods rely on 1-point RANSAC to reject outliers under the assumption of a static world, thereby discarding dynamic landmarks as noise. However, in practice, outliers may arise not only from measurement errors but also from the motion of objects. To address this issue, we propose a modified 1-point RANSAC framework that detects dynamic objects and leverages both static and dynamic landmarks for ego-motion estimation. Inspired by adaptive strategies observed in biological vision systems, our approach integrates EKF-based state estimation with dynamic object tracking to achieve simultaneous ego-motion and object-motion estimation, improving robustness in complex and dynamic scenes.

Source: Biomimetics

DOI: 10.3390/biomimetics10100710

Authors: Jinhee Lee, Jaeyoung Kang

Institution: Inha University

Language: English

Topic: Robotics and Sensor-Based Localization

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,654

Funder: Inha University