BlabberSeg: Semantic Perception for Reliable Open-Vocabulary UAV Safe Landing

Haechan Mark Bong, Giovanni Beltrame

Keywords: UAV, VLM, Safe-Landing, Autonomy

Abstract: Reliable robot autonomy requires semantic perception that remains both informative and fast enough for closed-loop safety decisions. We present BlabberSeg, an optimized CLIPSeg-based open-vocabulary segmentation pipeline for UAV emergency landing. The method targets semantic reliability under edge constraints by reusing prompt, positional, and image features and deploying floating-point 16 ONNX (TensorRT) inference. In a DOVESEI-based safe-landing workflow, BlabberSeg reaches 16.78Hz on Jetson Orin AGX (64GB), a 927.41% speed increase over the original CLIPSeg (1.81Hz), with limited degradation in segmentation agreement (2.1% relative area difference) and mIoU (9%). At the task level, safe-landing success is preserved (76/100, matching baseline) while mission time is substantially reduced. These results support semantic open-vocabulary perception as a practical component for reliable autonomous landing.

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Submission Number: 14