动态环境中移动机器人定位与环境状态估计新算法

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"本文提出了一种新的室内移动机器人在动态环境中定位和状态估计的方法，它结合了粒子滤波器和条件二元贝叶斯滤波器，能够在高维度的估计问题中有效地解决机器人定位和环境状态监测。" 移动机器人在室内环境中定位是一项关键任务，传统方法通常依赖于静态地图，但在实际应用中，环境常常会发生变化，如门的开关等动态因素。在这种情况下，同时估计机器人的位置和环境的状态变得非常复杂，因为这涉及一个高维的估计问题，超出了一些现有定位技术的能力范围。本文针对这一挑战，提出了一种高效的分式估计算法，适用于混合离散-连续状态的估计。该算法的核心是整合了两个关键组件：一是粒子滤波器，用于机器人自身的定位；二是条件二元贝叶斯滤波器，专门用于估计环境的动态状态，比如门的开闭状态。粒子滤波器是一种概率建模技术，能够处理非线性和非高斯的动态系统，它通过大量的随机样本（即粒子）来近似表示后验概率分布，从而实现机器人的实时定位。而条件二元贝叶斯滤波器则是一种有效的处理二值或离散状态变化的工具，对于识别环境中的事件，如门的开启或关闭，尤其适用。实验结果显示，这种新算法在估计门的状态方面表现卓越，并且在动态环境下优于现有的先进定位器。这意味着即使环境发生变化，机器人也能准确地识别自身位置和周围环境的状态，这对于自主导航、避障以及环境监控等任务具有重要意义。这项研究为移动机器人在不断变化的室内环境中的定位和感知提供了新的思路，不仅提高了定位精度，还增强了机器人对环境动态变化的适应能力。未来，这种技术可能被广泛应用于智能家居、仓库管理、医疗护理等多个领域，推动移动机器人技术的进一步发展。

A Probabilistic Technique for Simultaneous Localization and Door State

Estimation with Mobile Robots in Dynamic Environments

Dzintars Avots

, Edward Lim

, Romain Thibaux

, Sebastian Thrun

Stanford University,



dzin,elim,thibaux



@cs.stanford.edu

Carnegie Mellon University, thrun@cs.cmu.edu

Abstract

Virtually all existing mobile robot localization techniques

operate on a static map of the environment. When the

environment changes (e.g., doors are opened or closed),

there is an opportunity to simultaneously estimate the

robot’s pose and the state of the environment. The re-

sulting estimation problem is high-dimensional, render-

ing current localization techniques inapplicable. This pa-

per proposes an efﬁcient, factored estimation algorithm

for mixed discrete-continuous state estimation. Our al-

gorithm integrates particle ﬁlters for robot localization,

and conditional binary Bayes ﬁlters for estimating the

dynamic state of the environment. Experimental results

illustrate that our algorithm is highly effective in estimat-

ing the status of doors, and outperforms a state-of-the-art

localizer in dynamic environments.

1 Introduction

In the past decade, mobilerobot localization and mapping

has received substantial interest in AI and robotics [1,

12]. The localization problem concerns itself with esti-

mating the pose of a robot relative to a ﬁxed map [9, 11],

whereas the mapping addresses the problem of learning a

map from sensor data [4, 13, 16].

A striking characteristic of the rich literature on this topic

is that virtually all published work assumes that the envi-

ronment is static. This is in contrast to most robotic en-

vironments, which usually change over time. For exam-

ple, most ofﬁce environments possess doors, chairs, and

other items whose location or state changes over time. A

worthwhile research goal, thus, is to extend existing tech-

niques by algorithms that can perform localization and

mapping functions in dynamic environments. Of course,

the recognition that natural environments are dynamic is

not new. For example, Fox et al. [8] have developed a

localization algorithm that is robust to the presence of

people in the environment, as demonstrated in a crowded

museum experiment [2]. However, such algorithms still

assume a static map, which is never revised in accordance

to sensor evidence. Instead, the paper is an example of a

more common methodology of treating dynamic effects.

In mobile robotics, dynamic effects are usually regarded

as noise and ﬁltered out [7].

This paper addresses a speciﬁc dynamic environment

problem: Estimating the state of a set binary state vari-

ables (e.g., doors, or grid cells in an occupancy grid map)

in an environment that changes. The difﬁculty of this

problem arises from the fact that localizing a robot in

such environment is generally difﬁcult. From an esti-

mation point of view, there is interaction between the

problem of estimating the robot’s location and the state

of the environment. For example, if a robot encounters

an obstacles in an area that previously corresponded to

an open door, there exist two quite complementary ways

to explain such measurements: Either the door status has

changed, or the robot is not where it believes to be (a less

plausible explanation would be that the observation is the

effect of sensor noise). As this example illustrates, the

problem of localization and environment state estimation

cannot be decoupled.

This paper proposes an efﬁcient algorithm for localizing

a robot in an environment with discrete states, while si-

multaneously estimating the state of the environment. We

show that under the appropriate formulation, the estima-

tion problem can be factored into a problem reminiscent

(but not identical) to conventional mobile robot localiza-

tion, and a number of conditional Bayes ﬁlters for esti-

mating the environment’s state. The speciﬁc algorithm

extends the work by Murphy [15] on state estimation in

Bayesian networks. Like Murphy, our approach employs

a particle ﬁlter for estimating the robot’s pose. The dis-

crete environment state variables are estimated via dis-

crete Bayesian ﬁlters (similar in nature to the standard

occupancy grid algorithm [14]). However, those Bayes

ﬁlters are conditioned on the robot’s path estimate, hence

are attached to individual particles. The resulting algo-

rithm scales linear in the number of state variables in the

environment, and linear in the number of particles. This

is signiﬁcantly more efﬁcient than existing existing map-

ping algorithms that combine localization and map esti-

mation [4, 13], which scale quadratically in the number

of environment state variables (and typically involve only

continuous state variables).

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