Problem
Research questions and friction points this paper is trying to address.
traversability
off-road navigation
self-supervised learning
lightweight inference
FLINT: Fast Lightweight Inference for Traversability
🤖 AI Summary
研究提出FLINT，一种轻量级通过性评估器，使用单一RGB相机实现高效准确的越野导航，优于重传感和计算的传统方法。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
lightweight traversability estimator
self-supervised learning
low computational cost
high accuracy
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