Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environments

TL;DR

Dream-SLAM introduces a novel active SLAM approach that leverages dreaming cross-spatio-temporal images and semantic scene structures to improve localization, mapping, and exploration in dynamic environments.

Contribution

It proposes a new monocular active SLAM method that integrates dreaming techniques for better long-term planning and dynamic scene handling.

Findings

Outperforms state-of-the-art in localization accuracy

Achieves higher mapping quality

Enhances exploration efficiency

Abstract

In addition to the core tasks of simultaneous localization and mapping (SLAM), active SLAM additionally in- volves generating robot actions that enable effective and efficient exploration of unknown environments. However, existing active SLAM pipelines are limited by three main factors. First, they inherit the restrictions of the underlying SLAM modules that they may be using. Second, their motion planning strategies are typically shortsighted and lack long-term vision. Third, most approaches struggle to handle dynamic scenes. To address these limitations, we propose a novel monocular active SLAM method, Dream-SLAM, which is based on dreaming cross-spatio-temporal images and semantically plausible structures of partially observed dynamic environments. The generated cross-spatio-temporal im- ages are fused with real observations to mitigate noise and data incompleteness, leading to more accurate camera pose estimation and a more coherent 3D scene representation. Furthermore, we integrate dreamed and observed scene structures to enable long- horizon planning, producing farsighted trajectories that promote efficient and thorough exploration. Extensive experiments on both public and self-collected datasets demonstrate that Dream-SLAM outperforms state-of-the-art methods in localization accuracy, mapping quality, and exploration efficiency. Source code will be publicly available upon paper acceptance.