الملخص
Autonomous nano-UAV navigation requires accurate ego-motion estimation under stringent size, weight, power, and computing (SWaP-C) constraints, where visual sensors and LiDARs exceed payload limits, optical flow degrades in low-texture scenes, and inertial-only state estimation is susceptible to accumulated drift. While multi-zone time-of-flight (ToF) arrays provide a lightweight metric complement, 6-DoF estimation from merely 384 ranges per frame is challenged by invalid returns, anisotropic observability, and temporal computational scaling. We propose TIO-FORMER, a camera-free, optical-flow-free, and mapless range-inertial odometry framework driven by an IMU and an ultra-lightweight (15 g) payload of six orthogonal 8 x 8 ToF arrays. Our frontend pairs consecutive range grids with a bilateral gated difference, while IMU-guided cross-attention dynamically routes directional features conditioned on platform kinematics. A Streaming Causal Transformer couples an uncompressed Local KV cache with compressed Chunk-FIFO memory, maintaining bounded inference cost and memory footprint independent of flight duration. In real-flight evaluations, TIO-FORMER reduces open-loop position error by 54.4% compared to nano-UAV optical flow and by 66.4%-89.1% over learned inertial baselines. We also evaluate performance across multiple environments and robustness under severe sensing degradation. Deployed on an edge RISC-V companion computer, TIO-FORMER achieves a P95 latency of 10.466 ms and peak resident memory of 6.324 MiB (less than 5 percent system RAM), demonstrating that sparse range sensing provides practical geometric anchoring for resource-constrained micro-aerial robots. Code is available at https://github.com/Ly041021/TIO-Former.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Liu, Y., He, Y., Zhao, W., Mo, X., Xu, Y., Wei, H., Ma, M., Li, H., Wu, Y., Gao, F., Dai, Z., & Zhou, X. (2026). TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer. https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer
MLA 9
Liu, Yang, et al. "TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer." https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer.
شيكاغو (المؤلف–التاريخ)
Liu, Yang, Yifan He, Wenhao Zhao, Xiangyu Mo, Yang Xu, Hao Wei, Mingze Ma, Huan Li, Yifan Wu, Fei Gao, Zipeng Dai, and Xin Zhou. 2026. "TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer." https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer.
هارفارد
Liu, Y., He, Y., Zhao, W., Mo, X., Xu, Y., Wei, H., Ma, M., Li, H., Wu, Y., Gao, F., Dai, Z. and Zhou, X. (2026) 'TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer', Available at: https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer.
فانكوفر
Liu Y, He Y, Zhao W, Mo X, Xu Y, Wei H, et al. TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer. https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer
IEEE
Y. Liu, Y. He, W. Zhao, X. Mo, Y. Xu, H. Wei, M. Ma, H. Li, Y. Wu, F. Gao, Z. Dai, and X. Zhou, "TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer," https://omanscience.com/ar/articles/tio-former-ultra-lightweight-6-directional-tof-inertial-odometry-for-nano-uavs-via-a-streaming-causal-transformer.