IMOST: Incremental Memory Mechanism with Online Self-Supervision for Continual Traversability Learning
Kehui Ma, Zhen Sun, Chaoran Xiong, Qiumin Zhu, Kewei Wang, Ling Pei

TL;DR
IMOST introduces an incremental memory and self-supervised annotation framework for continual traversability learning, improving scene understanding and adaptability in dynamic environments for robotic navigation.
Contribution
The paper presents IMOST, a novel continual learning framework with dynamic memory and real-time annotation, addressing existing SSL limitations in traversability estimation.
Findings
Outperforms state-of-the-art methods on multiple datasets.
Maintains robust recognition and adaptability in various scenarios.
Successfully deployed on a quadruped robot for online learning.
Abstract
Traversability estimation is the foundation of path planning for a general navigation system. However, complex and dynamic environments pose challenges for the latest methods using self-supervised learning (SSL) technique. Firstly, existing SSL-based methods generate sparse annotations lacking detailed boundary information. Secondly, their strategies focus on hard samples for rapid adaptation, leading to forgetting and biased predictions. In this work, we propose IMOST, a continual traversability learning framework composed of two key modules: incremental dynamic memory (IDM) and self-supervised annotation (SSA). By mimicking human memory mechanisms, IDM allocates novel data samples to new clusters according to information expansion criterion. It also updates clusters based on diversity rule, ensuring a representative characterization of new scene. This mechanism enhances scene-aware…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
