PredRecon: A Prediction-boosted Planning Framework for Fast and High-quality Autonomous Aerial Reconstruction (2302.04488v1)

Abstract: Autonomous UAV path planning for 3D reconstruction has been actively studied in various applications for high-quality 3D models. However, most existing works have adopted explore-then-exploit, prior-based or exploration-based strategies, demonstrating inefficiency with repeated flight and low autonomy. In this paper, we propose PredRecon, a prediction-boosted planning framework that can autonomously generate paths for high 3D reconstruction quality. We obtain inspiration from humans can roughly infer the complete construction structure from partial observation. Hence, we devise a surface prediction module (SPM) to predict the coarse complete surfaces of the target from the current partial reconstruction. Then, the uncovered surfaces are produced by online volumetric mapping waiting for observation by UAV. Lastly, a hierarchical planner plans motions for 3D reconstruction, which sequentially finds efficient global coverage paths, plans local paths for maximizing the performance of Multi-View Stereo (MVS), and generates smooth trajectories for image-pose pairs acquisition. We conduct benchmarks in the realistic simulator, which validates the performance of PredRecon compared with the classical and state-of-the-art methods. The open-source code is released at https://github.com/HKUST-Aerial-Robotics/PredRecon.

• The paper introduces a novel prediction-boosted planning framework that accelerates aerial reconstruction while ensuring high-quality results.
• It integrates predictive modeling with trajectory optimization to overcome local minima and enhance efficiency in complex 3D environments.
• Experimental evaluations demonstrate robust UAV performance, reducing collision risks and enabling safer autonomous operations.

Topology Graph Aided Gradient-based Trajectory Optimization for Robust UAV Replanning

This paper presents a significant advancement in the field of unmanned aerial vehicle (UAV) path planning by introducing a methodology that combines topology graphs with gradient-based trajectory optimization to enhance robust UAV replanning. The paper addresses key deficiencies in existing approaches by focusing on improving success rates in high-risk scenarios where initial trajectories may be in collision, and by alleviating local optimality issues inherent in gradient-based methods.

Core Contributions

The research identifies critical limitations of traditional gradient-based trajectory optimization methods which often necessitate good initial paths to avoid local minima and potential collisions. The proposed solution integrates topology graph techniques and gradient-based optimization to provide a more robust framework that considers non-convex environments and guarantees improved UAV performance under challenging conditions.

Key contributions of the paper include:

1. Topology-aided Gradient Optimization: By leveraging topology graphs, the authors address the limitation of local optimality, forging paths that are distinctively topological and less prone to become trapped in suboptimal local minima.
2. Homotopy and K-Order Deformability: The utilization of homotopic and K-order deformability relations enables the identification of rich sets of paths within varied 3D environments, surpassing the more common homotopy-based methods that often fall short in complex three-dimensional spaces.
3. Euclidean Signed Distance Field (EDF) Utilization: The implementation of EDF as part of the optimization process aids in maintaining safe path trajectories, contributing to the significant reduction of potential collisions.

Implications and Future Work

The introduction of topologically-informed path planning mechanisms not only ensures a higher success rate in collision-free path generation but also enhances the versatility of UAVs in complex three-dimensional environments. This work lays the groundwork for more sophisticated UAV path optimization strategies that are crucial for autonomous operations in dynamic and unpredictable environments.

Future research could focus on refining this integrated approach by expanding the topological methods to further align with real-time optimization demands and by exploiting advancements in sensor technology to dynamically update topology graphs. Additionally, exploring high-dimensional spaces and optimizing the computational efficiency of these solutions should remain a priority to enable scalability and deployment in larger-scale UAV missions.

By bridging the gap between theoretical path planning strategies and practical UAV applications, this paper provides pivotal insights into the effective deployment of UAV systems across various fields, including surveillance, delivery services, and aerial mapping, where robustness and reliability are paramount.