发表机构
Dalian University of Technology（大连理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UCON通过历史重关联和不确定性扇区嵌入轨迹优化，解决动态环境导航中的感知不稳定与不确定性优化不匹配问题，实现稳定感知与鲁棒避障。
AI 中文摘要
动态环境中的自主导航受到两个基本挑战的阻碍：感知不稳定性和不确定性优化不匹配。前者导致身份切换和不可靠的运动估计，而后者阻碍了将运动不确定性以原则性方式纳入轨迹优化。为解决这些挑战，我们提出了UCON，一种动态环境中的不确定性感知导航算法。针对感知不稳定性，我们提出了一种点级历史重关联机制，该机制利用历史点云片段来恢复丢失的目标，同时保持身份连续性。随后，采用卡尔曼滤波器提供各向异性的运动状态估计和协方差传播。为解决不确定性优化不匹配问题，我们将预测状态及其协方差转换为不确定性扇区，并将其作为可微成本项嵌入轨迹优化框架中。这实现了在保持平滑性和可行性的同时，进行一致的不确定性感知动态障碍物规避。大量的仿真和真实世界实验表明，在保持高计算效率的同时，与最先进的方法相比，UCON在动态环境中实现了优越的感知稳定性和鲁棒的导航性能。代码将开源以促进进一步研究。
英文摘要
Autonomous navigation in dynamic environments is hindered by two fundamental challenges: perception instability and uncertainty-optimization mismatch. The former leads to identity switches and unreliable motion estimation, while the latter prevents principled incorporation of motion uncertainty into trajectory optimization. To address these challenges, we propose UCON, an uncertainty-aware navigation algorithm in dynamic environments. For perception instability, we present a point-level historical re-association mechanism that leverages historical point cloud fragments to recover lost targets while maintaining identity continuity. Subsequently, a Kalman filter is employed to provide anisotropic motion state estimation and covariance propagation. To resolve the uncertainty-optimization mismatch, we transform predicted states and their covariances into uncertainty sectors, which are embedded as differentiable cost terms within a trajectory optimization framework. This achieves consistent uncertainty-aware dynamic obstacle avoidance while maintaining smoothness and feasibility. Extensive simulations and real-world experiments demonstrate that, while maintaining high computational efficiency, UCON achieves superior perception stability and robust navigation performance in dynamic environments compared to state-of-the-art methods. The code will be open-sourced to facilitate further research.
CommentsAccepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
