Towards object mapping in non-stationary environments with mobile robots

Biswas, Rahul; Limketkai, Benson; Sanner, Scott; Thrun, Sebastian

doi:10.1109/irds.2002.1041523

Cited by 89 publications

(77 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, neither offline approaches such as [31], which use the expectation maximization algorithm to differentiate between dynamic and static parts of the environment, are considered further nor methods trying to learn a limited number of configurations of dynamic objects as shown in [237] because objects in road environments move continuously and cannot be restricted to certain configurations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

View full text Add to dashboard Cite

The dissertation approaches the questions i) how to represent the driving environment in an environment model, ii) how to obtain such a representation, and iii) how to predict the traffic scene for criticality assessment. Bayesian inference provides the common framework of all designed methods. First, Parametric Free Space (PFS) maps are introduced, which compactly represent the vehicle environment in form of relevant, drivable free space. They are obtained by a novel method for grid mapping and tracking in dynamic environments. In addition, a maneuver-based, long-term trajectory prediction and criticality assessment system is introduced and the application of all methods within the advanced driver assistance system PRORETA 3 is described.

show abstract

Section: Related Workmentioning

confidence: 99%

“…They are target-oriented 31 Note that single-target filters can also be run in multi-target scenarios but with degraded performance. If targets are well-separated in the measurement space or otherwise distinguishable, a single-target filter is sufficient and explicit multi-target schemes just waste computational resources.…”

mentioning

confidence: 99%

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Schreier

2017

At - Automatisierungstechnik

View full text Add to dashboard Cite

show abstract

“…Recognizing that many objects in indoor human environments are of similar shapes, the approach of hierarchical object maps [7] assumes certain classes of shapes of objects that are matched to observed unmapped objects. The Robot Object Mapping Algorithm [8] detects moveable objects by detecting differences in the maps built by SLAM at different times. Detection and Tracking of Moving Objects [9] is an approach that seeks to detect and track moving objects while performing SLAM.…”

Section: Related Workmentioning

confidence: 99%

Model-Instance Object Mapping

Biswas

Veloso

2015

RoboCup 2014: Robot World Cup XVIII

View full text Add to dashboard Cite

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.

show abstract

“…[12] addressed the problem of localization in dynamic environments in an on-line manner using occupancy grid based representation, where both static and dynamic parts of the environment were represented in terms of separate occupancy grids. In the work of [13] the issue of dynamic changes have been tackled at the level of entire map using map differencing techniques and Expectation Maximization Algorithm; [14] proposed a method for on-line detection and identification of moving objects assuming ideal localization. The proposed work is the closest to [10,11] approaches to change detection.…”

Section: Related Workmentioning

confidence: 99%

Detecting Changes in Images of Street Scenes

Košecká

2013

Computer Vision – ACCV 2012

View full text Add to dashboard Cite

Abstract. In this paper we propose an novel algorithm for detecting changes in street scenes when the vehicle revisits sections of the street at different times. The proposed algorithm detects structural geometric changes, changes due to dynamically moving objects and as well as changes in the street appearance (e.g. posters put up) between two traversal times. We exploit geometric, appearance and semantic information to determine which areas have changed and formulate the problem as an optimal image labeling problem in the Markov Random Field framework. The approach is evaluated on street sequences from 3 different locations which were visited multiple times by the vehicle. The proposed method is applicable to monitoring and updating models and images of urban environments.

show abstract

Towards object mapping in non-stationary environments with mobile robots

Abstract: We

Cited by 89 publications

References 12 publications

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Bayesian environment representation, prediction, and criticality assessment for driver assistance systems

Model-Instance Object Mapping

Detecting Changes in Images of Street Scenes

Contact Info

Product

Resources

About