外观
论文arXiv 2609.171981 个月前
TIO-Former：基于流式因果 Transformer 的面向 Nano-UAVs 的超轻量级六方向 ToF-惯性里程计
原文链接：TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer（arXiv 2609.17198 · 发布日期：2026-09-15）
TIO-Former: Ultra-Lightweight 6-Directional ToF-Inertial Odometry for Nano-UAVs via a Streaming Causal Transformer
TIO-FORMER面向SWaP-C受限的nano-UAV自运动估计，融合六向ToF与IMU，并在真实飞行和边缘RISC-V上验证。
核心要点
- 视觉传感器和LiDAR受载荷限制，纯惯性易漂移，稀疏ToF估计6-DoF具挑战。
- 系统使用六个正交8×8 ToF阵列和IMU，结合双边门控差分与IMU引导交叉注意力。
- 流式因果Transformer结合Local KV与Chunk-FIFO记忆，约束长时推理成本和内存占用。
- 真实飞行中位置误差较光流降54.4%，较学习惯性基线降66.4%-89.1%，代码已开源。
验证范围与局限：验证范围包括真实飞行、多环境、严重感知退化测试及边缘RISC-V部署。
英文摘要 (Abstract)
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.
作者：Yang Liu, Yifan He, Wenhao Zhao, Xiangyu Mo, Yang Xu, Hao Wei, Mingze Ma, Huan Li, Yifan Wu, Fei Gao
来源
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