---
title: "从距离到轨迹：面向无人机的实时符号距离函数建图与距离加速运动规划"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19306v1"
permalink: "https://feed.astralor.com/items/itm_9c0f2d6602025bab78166b65"
published: "2026-07-21T17:18:46.000Z"
score: 80
channel: "ai"
tags: ["Bubble*","OREN","八叉树","无人机","符号距离函数","运动规划"]
---

# 从距离到轨迹：面向无人机的实时符号距离函数建图与距离加速运动规划

## AI 点评

> 这篇论文将SDF建图和规划巧妙地耦合在一起，用同一个表征贯穿全流程，思路非常漂亮。对做无人机自主飞行、移动机器人导航的研究者来说，OREN和Bubble*的设计都非常值得借鉴。特别是Bubble*利用距离信息直接生成空泡，极大减少了碰撞检测次数，很实用。

## 速览

在杂乱环境中，无人机需实时构建几何地图并规划安全轨迹。现有方法将建图与规划分离且依赖二值占据。本文提出共设计框架，基于符号距离函数（SDF）统一表征。开发OREN网络，结合显式八叉树先验与隐式神经残差，从点云在线重建高精度SDF；同时提出Bubble*搜索规划器，利用距离信息生成最大无碰撞球并构建图结构，显著减少碰撞检查。集成系统在四旋翼上实现实时导航，SDF估计提升22%，轨迹规划仅需1-3秒。

## 关键点

- 提出OREN网络，结合显式八叉树先验与隐式神经残差，实现高效、精确、可微的在线SDF重建。
- 提出Bubble*规划器，利用SDF距离信息生长最大无碰撞球，构建图结构进行搜索，具有终止性、完备性和失效检测保证。
- 集成OREN-Bubble*系统在四旋翼上实现实时导航，SDF估计提升22%，轨迹规划速度提升3-10倍。

## 全文

Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time. Conventional approaches treat mapping and planning as separate stages and often rely on binary occupancy for collision checking. We argue that these two stages should be co-designed around a single representation: a signed distance function (SDF). By encoding distance to the nearest obstacle, an SDF provides richer information for planning and trajectory optimization than occupancy alone. We develop an Octree REsidual Network (OREN) that pairs an explicit octree prior with an implicit neural residual to reconstruct SDFs online from point cloud observations with the efficiency of volumetric methods and the accuracy and differentiability of neural methods. In tandem, we develop Bubble$^\star$, a search-based planner that exploits the distance information to grow maximal collision-free balls, which we call bubbles, with formal guarantees of termination, completeness, and failure detection. Planning over a graph of bubbles significantly reduces collision checks compared to a grid-based A$^\star$ search and returns a bubble sequence that forms a safe corridor for trajectory optimization. We demonstrate the integrated OREN-Bubble$^\star$ approach onboard a quadrotor, navigating unseen indoor environments in real time under tight compute constraints. OREN improves SDF estimation by $22$% compared to baselines, while Bubble$^\star$ finds trajectories spanning $\approx 90$ m through a cluttered environment in $1$-$3$ sec., whereas baselines take up to $10$ sec. in the same environment.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_9c0f2d6602025bab78166b65