Long-term Autonomous Environment Adaptation of Mobile Robots: State-of-the-art Methods and Prospects
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摘要: 真实世界中存在光照、天气、季节及场景结构等复杂环境因素, 这些因素的改变对移动机器人基本行为和任务能力带来巨大挑战.随着机器人与人工智能技术的不断发展, 如何使移动机器人在长期运行中与复杂多变的环境条件相适应是智能机器人领域的研究热点.本文重点从地图构建与动态维护、重定位及场景理解等移动机器人基本行为能力的系统综述入手, 对移动机器人长期自主环境适应的前沿技术与研究方向进行了着重论述与分析.最后对该领域的研究重点和技术发展趋势进行了探讨.Abstract: In real-world applications, mobile robots will work in complex open environments, where there are various changing factors such as time-varying illumination, weather, seasons and scene layout, which are all challenging tasks for a mobile robot with a long-term autonomous environment adaptation ability. With the rapid development and improvement of the technology in mobile robotics and artificial intelligence, how to make mobile robots adapt to changing environments is a hot research topic. This paper starts with a systematic review of the basic capabilities of mobile robots such as mapping and map updating, relocalization and scene understanding in dynamic environments, and then focuses on the cutting-edge technologies of long-term autonomous environment adaptation of mobile robots. The research emphases and prospective technical development trends are also presented at the end of this paper.
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Key words:
- Environment adaptation /
- long-term autonomy /
- mapping /
- relocalization /
- scene understanding /
- mobile robots
1) 本文责任编委 吴立刚 -
表 1 动态环境下长航时机器人地图构建与长期维护方法
Table 1 Methods of mapping and long-term maintenance for mobile robots in dynamic environment
测试场景 关键词 主要思想 局限性 文献 停车场 临时地图 用临时地图跟踪由环境中半静态物体引起的矛盾观测, 并临时地扩展环境的静态地图, 进而利用粒子滤波算法实现机器人的定位 该算法默认动态物体被成功地检测并滤除 [24] 动态栅格地图 将每个栅格定义为隐式马尔科夫模型, 利用初始状态分布和观测模型预测状态转换概率 模型假设与真实环境中的动态变化的一致性问题; 栅格与栅格之间的独立假设不合理 [23] 非马尔科夫过程; 插曲片段 释放了马尔科夫过程的独立性假设, 分析观测数据与地图中未标记的物体之间的关联; 利用关联分析将对环境的观测分为长期、短期和动态特征 依赖提前构建的地图先验信息 [26] 生产车间 独立马尔科夫链; 栅格地图 将每个栅格定义为有两个状态的独立马尔科夫链; 栅格状态转换被定义为两个泊松过程, 且转换模型参数通过在线学习获得 算法模型假设环境中的动态变化属于齐次过程, 而事实上动态变化是有时间依赖性的 [25] 正态分布变换栅格地图 结合正态分布变换地图的简洁和栅格地图的稳定来表述动态环境, 并定义了精确的栅格状态递归更新模型; 该方法可用于多分辨率地图 该方法依赖一个外部准确的定位系统 [7] 室内环境 记忆消退 利用多重时间尺度下的环境模型表述环境; 随时间推移, 新的环境模型不断更新, 旧的不断消退 需要不断地对环境进行访问和建模; 只适应渐进的环境变化 [27] 多重记忆存储模型 模仿人类多重记忆模型, 用选择机制将环境观测分为传感器记忆、临时记忆和永久记忆; 并利用永久记忆对地图进行更新维护 地图中缺少尺度度量信息, 且只考虑了环境中有限的环境变化 [28] 多重记忆存储模型/3D构图 该方法利用短期记忆和永久记忆机制, 保证只利用永久的环境信息构建地图 环境中稳定的元素需要经常被观测和识别才能加入到永久记忆中 [29] 频谱分析 利用频谱对环境中的时空动态变化进行建模; 较小的存储需求适用于大范围环境 假设人类的行为是有规律可循的, 只适用于部分情形 [30] 摘要地图 摘要地图中只保存被认为有用的路标信息 地图中有限的环境信息, 只适用于解决特定的任务 [36] 城市环境 端到端分割聚类 搭建了输入是原始点云数据、输出是分割聚类结果的端到端架构; 利用二分类解决多分类问题 只考虑了车辆、行人和自行车三类动态目标 [32] 统一的栅格环境模型 构建了新颖的基于栅格的环境模型, 其中对动静态物体及其不确定性、速度等特征进行统一建模 利用栅格地图表述城市环境, 存在栅格分辨率选择和边缘混淆问题 [33] 校园环境 无监督增量学习 利用AP聚类算法对三维点云进行聚类; 并通过机器人与环境的交互得到聚类目标是障碍的概率 该方法只判断聚类目标是否为障碍, 没有进一步估计其速度等特性 [34] 地图长期维护 构建了包含位姿估计、全局地图维护和速度估计三个模块长期定位与构图系统, 通过对环境的重复观测, 直接对三维点进行状态预测与更新 该方法假设动态目标运动平滑 [35] 
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表 2 移动机器人基于人工设计图像特征的重定位方法
Table 2 Visual methods of relocalization based on hand-crafted features for mobile robots in term of long-term autonomy
目标环境 关键词 主要思想 发表年份 文献 光照/季节/环境结构变化 基于经验描述的重定位 将环境的模型表述定义为一条"经验", 利用视觉相对定位将场景的多种经验串联起来 2013 [49] 光照/季节变化 基于图像序列的重定位 利用较长的图像序列代替单幅图像实现场景匹配, 完成重定位 2012 [50] 光照条件变化 光照不变性图像 将RGB图像转换为具有光照不变性的图像, 进而利用FAST特征检测器和BRIEF特征描述符实现场景匹配 2014 [54] 光照/季节变化 光照不变性图像; 图像序列 利用光照不变性图像和图像序列等技术手段实现重定位; 同时提取全局二进制描述符来提高效率 2018 [56] 光照/季节变化 外观变化预测 鉴于自然条件下环境外观呈现周期性变化, 该方法通过预测不同条件下的环境外观来实现长期重定位 2013 [57] 光照/季节/动态因素 场景动态模型 通过分析场景中各种动态元素对局部特征的影响, 学习并利用场景中稳定的静态特征, 实现鲁棒的重定位 2013 [60]
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表 3 移动机器人基于三维点云的重定位方法
Table 3 Methods of relocalization based on point clouds for mobile robots in term of long-term autonomy
算法类型 主要思想 局限性 发表年份 文献 直接法 对三维点云进行降采样, 利用关键点投票的方法实现高效的场景配准 不能解决环境的结构变化 2013 [70] 特征法 从三维点云中提取线性特征、面性特征以及球性特征, 并利用对不同类型特征的分布统计来实现场景间的高效匹配 众多的参数调节, 时间成本高 2009 [71] 将三维点云转换为二维图像, 利用视觉的方法实现机器人的重定位 生成二维图像不具有视点不变性 2018 [73] 将三维点云向若干个平面投影, 统计每个平面上点的投影分布生成全局特征, 进而实现场景匹配 对季节变化敏感 2016 [76] 分割聚类 对三维点云进行分割聚类, 利用聚类目标替代特征点实现场景匹配 对车辆等可能被移动的半静态物体敏感 2017 [75] 基于经验的方法 通过对同一场景的重复观测, 捕获其在结构或外观上的变化, 进而利用基于经验的方法实现机器人的长期稳定重定位 为了捕获某一场景的所有变化, 需要不断对该场景重复访问, 代价昂贵, 在大范围场景中易造成信息爆炸 2015 [77]
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表 4 动态环境下长航时机器人自主场景理解方法
Table 4 Methods of scene understanding for mobile robots in term of long-term autonomy in dynamic environment
关键词 主要思想 目标问题 发表年份 文献 经典学习方法 将三维点云转换为二维图模型, 利用图像纹理和尺度协调技术解决多尺度分类问题 尺度不变性 2015 [85] 深度学习方法 利用大量训练数据训练目标识别模型, 利用训练数据的多样性解决多视角/多尺度目标识别 视角/尺度不变性 2015 [87] 结合深度学习和立体视觉重构算法实现了具有尺度不变性的语义分割方法 尺度不变性 2016 [92] 利用深度信息学习网络估计充足的深度, 进而利用深度信息调整分割网络中池化域的尺寸, 进而实现了具有尺度不变性的语义场景分割网络 尺度不变性 2018 [93] 利用图像和三维点云联合训练语义分割网络结构, 学习并结合2D和3D特征, 实现鲁棒的语义场景分割方法 光照/季节不变性 2018 [94] 迁移学习方法 提出基于在线学习的跨领域特征变换算法, 并结合$k$-NN分类器实现了跨领域的多类物体识别 跨领域多分类 2018 [88] 通过跨领域动态合成实例和"有选择性"地进行实例迁移来再平衡目标领域中训练数据的类分布 类不平衡问题 2017 [89] 通过共享碎片特征的方式联合训练多个目标检测器, 并且提出了一个基于数据采样技术的类不平衡算法, 对低置信率检测输出矩形框的再分类进一步提高了多类目标检测的准确率 类不平衡/小样本问题 2018 [90]
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