Dynamic Maps for Long-Term Operation of Mobile Service Robots

Biber, Peter; Duckett, Tom

doi:10.15607/rss.2005.i.003

Cited by 119 publications

(103 citation statements)

References 6 publications

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“…This is clearly a simplification of the real world since there are objects that can act as static or dynamic; for instance, doors, chairs, tables, cars, etc. Although there exist some preliminary work on the estimation of the state of some of these objects [47,6], we believe a prior on the behavior of the objects will greatly simplify the problem. This can be done using another type of sensors as cameras or 3D range sensors.…”

Section: Discussionmentioning

confidence: 99%

Modeling dynamic scenarios for local sensor-based motion planning

2008

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This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in the modeling aspect is a formulation of the detection and tracking of mobile objects and the mapping of the static structure in such a way that the nature (static/dynamic) of the observations is included in the estimation process. The algorithm provides a set of filters tracking the moving objects and a local map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based motion planning system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

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Section: Discussionmentioning

confidence: 99%

Modeling dynamic scenarios for local sensor-based motion planning

2008

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“…In a very recent work, Bieber and Duckett [7] proposed an approach that incorporates changes of the environment into the map representation. Compared to our work, they model temporal changes of local maps whereas we aim to identify the different configurations of the environment.…”

Section: Related Workmentioning

confidence: 99%

Mobile Robot Map Learning from Range Data in Dynamic Environments

Burgard

Stachniss

Hähnel

Springer Tracts in Advanced Robotics

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Summary. The problem of generating maps with mobile robots has received considerable attention over the past years. Most of the techniques developed so far have been designed for situations in which the environment is static during the mapping process. Dynamic objects, however, can lead to serious errors in the resulting maps such as spurious objects or misalignments due to localization errors. In this chapter, we consider the problem of creating maps with mobile robots in dynamic environments. We present two approaches to deal with non-static objects. The first approach interleaves mapping and localization with a probabilistic technique to identify spurious measurements. Measurements corresponding to dynamic objects are then filtered out during the registration process. Additionally, we present an approach that learns typical configurations of dynamic areas in the environment of a mobile robot. Our approach clusters local grid maps to identify the typical configurations. This knowledge is then used to improve the localization capabilities of a mobile vehicle acting in dynamic environments. In practical experiments carried out with a mobile robot in a typical office environment, we demonstrate the advantages of our approaches.

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“…Dynamic Pose Graph SLAM [2] is one such approach that extends pose graph SLAM. Dynamic Maps [3] extends SLAM and maintains estimates of the map over several timescales using recency weighted samples of the map at several timescales simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Model-Instance Object Mapping

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Robot localization and mapping algorithms commonly represent the world as a static map. In reality, human environments consist of many movable objects like doors, chairs and tables. Recognizing that such environment often have a large number of instances of a small number of types of objects, we propose an alternative approach, Model-Instance Object Mapping that reasons about the models of objects distinctly from their different instances. Observations classified as short-term features by Episodic non-Markov Localization are clustered to detect object instances. For each object instance, an occupancy grid is constructed, and compared to every other object instance to build a directed similarity graph. Common object models are discovered as strongly connected components of the graph, and their models as well as distribution of instances saved as the final Model-Instance Object Map. By keeping track of the poses of observed instances of object models, Model-Instance Object Maps learn the most probable locations for commonly observed object models. We present results of Model-Instance Object Mapping over the course of a month in our indoor office environment, and highlight the common object models thus learnt in an unsupervised manner.

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Dynamic Maps for Long-Term Operation of Mobile Service Robots

Cited by 119 publications

References 6 publications

Modeling dynamic scenarios for local sensor-based motion planning

Modeling dynamic scenarios for local sensor-based motion planning

Mobile Robot Map Learning from Range Data in Dynamic Environments

Model-Instance Object Mapping

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