论文标题
基于稀疏的贝叶斯内核映射的未知环境中的自主导航
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
论文作者
论文摘要
本文着重于在线占用映射和实时碰撞检查在大型未知环境中导航的自动驾驶机器人。常用的体素和OCTREE地图表示形式可以轻松地保持在一个小的环境中，但随着环境的增长，内存需求的增加。我们提出了一种根本不同的占用映射方法，其中占用空间和自由空间之间的边界被视为机器学习分类器的决策边界。这项工作概括了一个内核感知器模型，该模型保持了一组非常稀疏的支持向量，以有效地表示环境边界。我们根据相关矢量机制定了一种概率公式，从而使稳健性得以测量噪声和概率占用分类，从而支持自主导航。我们提供了一种在线培训算法，从流范围数据中逐渐更新稀疏的贝叶斯地图，以及用于一般曲线的有效碰撞检查方法，代表潜在的机器人轨迹。我们的映射和碰撞检查算法的有效性在需要自动驾驶机器人导航和主动映射的任务中评估。
This paper focuses on online occupancy mapping and real-time collision checking onboard an autonomous robot navigating in a large unknown environment. Commonly used voxel and octree map representations can be easily maintained in a small environment but have increasing memory requirements as the environment grows. We propose a fundamentally different approach for occupancy mapping, in which the boundary between occupied and free space is viewed as the decision boundary of a machine learning classifier. This work generalizes a kernel perceptron model which maintains a very sparse set of support vectors to represent the environment boundaries efficiently. We develop a probabilistic formulation based on Relevance Vector Machines, allowing robustness to measurement noise and probabilistic occupancy classification, supporting autonomous navigation. We provide an online training algorithm, updating the sparse Bayesian map incrementally from streaming range data, and an efficient collision-checking method for general curves, representing potential robot trajectories. The effectiveness of our mapping and collision checking algorithms is evaluated in tasks requiring autonomous robot navigation and active mapping in unknown environments.
