论文标题
使用稀疏内核的占用映射在未知环境中的自主导航
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
论文作者
论文摘要
本文着重于实时占用映射和碰撞检查在未知环境中导航的自动驾驶机器人。我们提出了一个新的地图表示形式，其中占用和自由空间与内核perceptron分类器的决策边界分开。我们开发了一种在线培训算法，该算法保持了一组非常稀疏的支持向量，以表示配置空间中的障碍边界。我们还得出了允许完成（不采样）碰撞检查的条件，以进行分段线性和分段多项式机器人轨迹。我们证明了映射和碰撞检查算法的有效性，以在未知环境中自动导航Ackermann-Drive机器人。
This paper focuses on real-time occupancy mapping and collision checking onboard an autonomous robot navigating in an unknown environment. We propose a new map representation, in which occupied and free space are separated by the decision boundary of a kernel perceptron classifier. We develop an online training algorithm that maintains a very sparse set of support vectors to represent obstacle boundaries in configuration space. We also derive conditions that allow complete (without sampling) collision-checking for piecewise-linear and piecewise-polynomial robot trajectories. We demonstrate the effectiveness of our mapping and collision checking algorithms for autonomous navigation of an Ackermann-drive robot in unknown environments.
