---
title: "From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs"
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","Octree","UAV","SDF","Motion Planning"]
---

# From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs

## AI commentary

> This paper elegantly couples SDF mapping and planning with a single representation throughout the pipeline, making the approach very appealing. For researchers in UAV autonomy and mobile robot navigation, OREN and Bubble* offer valuable design insights. Bubble*'s use of distance information to directly generate collision-free bubbles significantly reduces collision checks, which is highly practical.

## At a glance

In cluttered environments, autonomous UAVs need to build geometric maps and plan safe trajectories in real time. Current methods treat mapping and planning separately and rely on binary occupancy. This paper proposes a co-designed framework based on a signed distance function (SDF). It develops OREN, an Octree REsidual Network that combines explicit octree priors with implicit neural residuals for online high-accuracy SDF reconstruction from point clouds. It also introduces Bubble*, a search-based planner that uses distance information to grow maximal collision-free balls and builds a graph, significantly reducing collision checks. The integrated system runs onboard a quadrotor in real time, improving SDF estimation by 22% and planning trajectories over 90 m in 1-3 seconds, compared to baselines taking up to 10 seconds.

## Key points

- Proposes OREN, combining explicit octree priors with implicit neural residuals for efficient, accurate, and differentiable online SDF reconstruction.
- Introduces Bubble*, a planner that grows maximal collision-free balls using SDF distance, building a graph with guarantees of termination, completeness, and failure detection.
- The integrated OREN-Bubble* system achieves real-time navigation on a quadrotor, improving SDF estimation by 22% and trajectory planning speed by 3-10x.

## Full text

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.

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