📝 SummarXiv

Abstract

To perform outdoor autonomous visual navigation and search, a robot may leverage satellite imagery as a prior map. This can help inform high-level search and exploration strategies, even when such images lack sufficient resolution to allow for visual recognition of targets. However, there are limited training datasets of satellite images with annotated targets that are not directly visible. Furthermore, approaches which leverage large Vision Language Models (VLMs) for generalization may yield inaccurate outputs due to hallucination, leading to inefficient search. To address these challenges, we introduce Search-TTA, a multimodal test-time adaptation framework with a flexible plug-and-play interface compatible with various input modalities (e.g. image, text, sound) and planning methods. First, we pretrain a satellite image encoder to align with CLIP's visual encoder to output probability distributions of target presence used for visual search. Second, our framework dynamically refines CLIP's predictions during search using a test-time adaptation mechanism. Through a novel feedback loop inspired by Spatial Poisson Point Processes, uncertainty-weighted gradient updates are used to correct potentially inaccurate predictions and improve search performance. To train and evaluate Search-TTA, we curate AVS-Bench, a visual search dataset based on internet-scale ecological data that contains up to 380k training and 8k validation images (in- and out-domain). We find that Search-TTA improves planner performance by up to 30.0%, particularly in cases with poor initial CLIP predictions due to limited training data. It also performs comparably with significantly larger VLMs, and achieves zero-shot generalization to unseen modalities. Finally, we deploy Search-TTA on a real UAV via hardware-in-the-loop testing, by simulating its operation within a large-scale simulation that provides onboard sensing.