Benchmarking Metric Ground Navigation
Benchmarking Metric Ground Navigation
复制标题
地面导航指标基准测试
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
P. Stone
中科院分区：
文献类型：
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作者：
Daniel Perille;Abigail Truong;Xuesu Xiao;P. Stone
米制地面导航解决了机器人在障碍物占据的平面环境中以无碰撞的方式从一点自主移动到另一点的问题。这是智能移动机器人最基本的能力之一。本文提出了一个标准化的试验台，并提供了一组环境和指标，以测试不同场景的难度和不同系统的性能。目前的基准测试主要集中在移动机器人导航的单个组件上，例如感知和状态估计，但导航性能作为一个整体很少以系统和标准化的方式进行测量。因此，导航系统通常以一种特别的方式进行测试和比较，例如在一个或两个手动选择的环境中。所介绍的基准为地面机器人在度量世界中的导航提供了一个通用的测试平台。自主机器人导航基准（BARN）数据集包括300个导航环境，这些导航环境由一组难度指标排序。导航性能可以在这些环境中以系统和客观的方式进行测试和比较。该基准可用于预测新环境的导航难度，比较导航系统，并可能作为基于计划和基于学习的导航系统的成本函数和课程。我们已经在www.cs.utexas.edu/~attruong/metric_dataset.html上发布了我们的数据集和源代码，以生成不同机器人足迹的数据集。
Metric ground navigation addresses the problem of autonomously moving a robot from one point to another in an obstacle-occupied planar environment in a collision-free manner. It is one of the most fundamental capabilities of intelligent mobile robots. This paper presents a standardized testbed with a set of environments and metrics to benchmark difficulty of different scenarios and performance of different systems of metric ground navigation. Current benchmarks focus on individual components of mobile robot navigation, such as perception and state estimation, but the navigation performance as a whole is rarely measured in a systematic and standardized fashion. As a result, navigation systems are usually tested and compared in an ad hoc manner, such as in one or two manually chosen environments. The introduced benchmark provides a general testbed for ground robot navigation in a metric world. The Benchmark for Autonomous Robot Navigation (BARN) dataset includes 300 navigation environments, which are ordered by a set of difficulty metrics. Navigation performance can be tested and compared in those environments in a systematic and objective fashion. This benchmark can be used to predict navigation difficulty of a new environment, compare navigation systems, and potentially serve as a cost function and a curriculum for planning-based and learning-based navigation systems. We have published our dataset and the source code to generate datasets for different robot footprints at www.cs.utexas.edu/~attruong/metric_dataset.html.
影响因子：
9.2
作者：
Geiger, A.;Lenz, P.;Urtasun, R.
通讯作者：
Urtasun, R.
影响因子：
9.2
作者：
Maddern, Will;Pascoe, Geoffrey;Newman, Paul
通讯作者：
Newman, Paul