Purpose

This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.

Design/methodology/approach

The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.

Findings

Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.

Practical implications

This paper provides comprehensive experiments in real industrial environments.

Originality/value

The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

中文翻译：

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复杂工业环境中的无地图长期定位

目的

本文旨在研究复杂工业环境中自主工业车辆的定位问题。以实际应用为目标，追求的是构建一个可以在存在动态障碍、短期和长期环境变化的情况下使用的无地图定位系统。

设计/方法/方法

该系统包含四个主要模块，包括长期位置图更新、全局定位和重新定位、位置跟踪和姿势注册。前两个模块充分利用基于深度学习的三维点云学习技术来实现大规模环境下的无地图全局定位任务。位置跟踪模块使用新设计的感知模型实现粒子滤波器框架，以在移动过程中跟踪车辆位置。最后，姿态配准模块利用视觉信息排除动态障碍物和短期变化的影响，并进一步引入点云配准网络来估计准确的车辆姿态。

发现

在真实工业环境中的综合实验证明了无地图定位方法的有效性、鲁棒性和实际适用性。

实际影响

本文提供了在真实工业环境中的综合实验。

原创性/价值

该系统可用于实际的自动化工业车辆，以执行长期定位任务。工业车辆的动态对象、短期/长期环境变化和硬件限制都在系统设计中得到考虑。因此，这项工作朝着在实际工业场景中真正实现自主定位迈出了一大步。