# 使用分布式低成本 Time-of-Flight 传感器的四足机器人避障与落足规划

> 原文链接：https://robotics.hellobike.com/papers/2026-09-10-2609-31008
> 频道：papers | 更新日期：2026-09-10 | 标签：arxiv, cs.RO
> 核心摘要：研究用分布式低成本 ToF 传感器替代深度相机进行近场地形映射，并在 ANYmal 四足上验证避障与落足规划。

# 使用分布式低成本 Time-of-Flight 传感器的四足机器人避障与落足规划

::: v-pre
> 原文链接：[Quadruped Obstacle Avoidance and Footstep Planning With Distributed Low-Cost Time-of-Flight Sensors](https://arxiv.org/abs/2609.31008)（arXiv 2609.31008 · 发布日期：2026-09-10）

**Quadruped Obstacle Avoidance and Footstep Planning With Distributed Low-Cost Time-of-Flight Sensors**

研究用分布式低成本 ToF 传感器替代深度相机进行近场地形映射，并在 ANYmal 四足上验证避障与落足规划。

## 核心要点

- 四足机器人常用深度相机和 LiDAR 建图，但近距离覆盖有限、成本较高且功耗较大。
- 作者为 ANYbotics ANYmal 设计分布式 ToF 感知架构，以减少传统传感器盲区。
- 实验评估环境重建精度，并与深度相机在地形映射和避障上进行基准比较。
- 结果表明其虽分辨率低且噪声较高，仍能以厘米级局部建图支持可靠感知运动。

**验证范围与局限**：验证范围为 ANYbotics ANYmal 四足机器人的近场地形映射、局部导航、避障与落足规划。

## 英文摘要 (Abstract)

Quadruped robots typically rely on depth cameras and LiDAR sensors to map their local environment. However, these sensors have limited close-range coverage, are relatively expensive, and consume significant power. This study investigates whether distributed Time-of-Flight (ToF) sensors can serve as a low-cost alternative to depth cameras for near-field terrain mapping for locomotion and local navigation. We designed a distributed ToF sensing architecture for the ANYbotics ANYmal quadruped, assessed its environment reconstruction accuracy, and benchmarked it against depth cameras for terrain mapping and obstacle avoidance. Distributing these sensors around the robot can also avoid the blind spots of traditional sensors. Our results show that, despite their low resolution and higher measurement noise, distributed ToF sensors can support reliable perceptual locomotion with centimeter-level local mapping accuracy. The proposed sensing strategy provides sufficient geometric information for near-field obstacle avoidance and footstep planning, at substantially lower cost, energy consumption, and system complexity than depth cameras.

作者：Giammarco Caroleo, Timothee Mahamoodally, Matteo Manzardo, Jin Jin, Marco Pontin, Matías Mattamala, Renato Vidoni, Perla Maiolino, Maurice F. Fallon
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## 原始参考来源 (Sources)

- [arXiv 2609.31008：Quadruped Obstacle Avoidance and Footstep Planning With Distributed Low-Cost Time-of-Flight Sensors](https://arxiv.org/abs/2609.31008)