FLINT：用于可通行性的快速轻量级推理

中文摘要 AI 辅助

由于缺乏结构，越野条件下的导航具有挑战性。对于什么是可通行的，没有固定的词汇。可通行性既取决于环境，也取决于载体的动力学。这两个变量都无法大规模地手工标注。因此，可通行性必须通过载体自身的经验来学习。现代平台倾向于使用多种传感器来估计可通行性并导航：RGBD相机、激光雷达、雷达、IMU，以及用于在神经网络上运行推理的计算密集型平台。针对这一趋势，我们提出了FLINT，一种轻量级的可通行性估计器：一个21.6M参数的骨干网络，比同类基础模型骨干网络小38倍，在保留的地形探针上得分更高，并且仅使用RGB相机作为唯一传感器，在CPU上以14.7 FPS运行。尽管规模存在差距，FLINT在24个重放野外日志中的23个上，比已部署的基础模型系统（WildOS）生成了更便宜、更准确的代价地图。我们比较了不同的自监督学习信号，并在闭环现场试验中将所得模型部署在真实平台上：最佳自监督头在路线上达到99%的自主性，优于在同一路线上实时部署的基于人工标注训练的基线。我们的结果表明，重型传感和计算对于可通行性估计并非必要。

英文摘要

Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversability depends on both the environment and the embodiment's dynamics. Neither of these two variables can be hand-labeled at scale. Thus, traversability has to be learned by the embodiment's own experience. Modern platforms tend to use multiple sensors to estimate traversability and navigate: RGBD cameras, lidar, radar, IMU, with computationally intensive platforms to run inference on neural networks. Against this trend, we propose FLINT, a lightweight traversability estimator: a 21.6M-parameter backbone, 38\times smaller than a comparable foundation-model backbone, that scores higher on held-out terrain probes and runs at 14.7 FPS on CPU alone using a RGB camera has the only sensor. Despite that gap in scale, FLINT produces a cheaper, more accurate costmap than a deployed foundation-model system (WildOS) on 23 of 24 replayed field logs. We compare different self-supervised learning signals and deploy the resulting models on a real platform in closed-loop field trials: the best self-supervised head reaches 99% autonomy over the route, outperforming a human-label-trained baseline deployed live on the same course. Our results show that heavy sensing and computing are not necessary for traversability estimation.

发表机构

• McGill University（麦吉尔大学）
• Centre de Technologies Avancées (CTA)（先进技术中心）
• Université de Sherbrooke（舍布鲁克大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

8 pages, 5 figures