Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment

Arbuckle, D.; Howard, Andrew; Matarić, Maja J.

doi:10.1109/irds.2002.1041424

Cited by 41 publications

(34 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…RELATED WORK In the study of dynamics in the environment, many methods are based on an underlying static representation, but extending it into a timescale framework or applying recency-weighted techniques. Arbuckle et al [7] extended the occupancy grid map by maintaining multiple occupancy representations corresponding to various timescales. With the resulting temporal occupancy grid (TOG), the dynamics of each cell is classified by the occupancy values over different timescales.…”

Section: Ieee/rsj International Conference Onmentioning

confidence: 99%

“…Occupancy grids [3] are one of the most popular representations of environments where mobile robots operate, and are also used for dynamic environment studies [4] [5] [6] [7]. In a very recent work, Meyer-Delius et al [8] generalized the occupancy grid map and released the static environment assumption by introducing the hidden Markov model (HMM).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling motion patterns of dynamic objects by IOHMM

Wang

Ambruş

Folkesson

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-This paper presents a novel approach to model motion patterns of dynamic objects, such as people and vehicles, in the environment with the occupancy grid map representation. Corresponding to the ever-changing nature of the motion pattern of dynamic objects, we model each occupancy grid cell by an IOHMM, which is an inhomogeneous variant of the HMM. This distinguishes our work from existing methods which use the conventional HMM, assuming motion evolving according to a stationary process. By introducing observations of neighbor cells in the previous time step as input of IOHMM, the transition probabilities in our model are dependent on the occurrence of events in the cell's neighborhood. This enables our method to model the spatial correlation of dynamics across cells. A sequence processing example is used to illustrate the advantage of our model over conventional HMM based methods. Results from the experiments in an office corridor environment demonstrate that our method is capable of capturing dynamics of such human living environments.

show abstract

Section: Ieee/rsj International Conference Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling motion patterns of dynamic objects by IOHMM

Wang

Ambruş

Folkesson

2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…In this approach, we use more than one occupancy grids (Arbuckle et al, 2002) (Biber & Duckett, 2005). Every occupancy grid has a particular timescale, which indicates how often it is refreshed (e.g.…”

Section: Occupancy Grids With Different Timescalesmentioning

confidence: 99%

“…One approach that makes use of this technique is the so-called temporal occupancy grid (TOG) (Arbuckle et al, 2002). It was introduced as a way to classify cells based on their time properties of occupancy.…”

Section: Occupancy Grids With Different Timescalesmentioning

confidence: 99%

See 1 more Smart Citation

An Introduction to the Problem of Mapping in Dynamic Environments

Mitsou¹,

Tzafestas²

2008

Tools in Artificial Intelligence

View full text Add to dashboard Cite

Simultaneous egomotion estimation, segmentation, and moving object detection

Yang

Wang

2011

Journal of Field Robotics

View full text Add to dashboard Cite

Robust egomotion estimation is a key prerequisite for making a robot truly autonomous. In previous work, a multimodel extension of random sample consensus (RANSAC) was introduced to deal with environments with rapid changes by incorporating moving object information. A multiscale matching algorithm was also proposed to resolve the issue of imperfect segmentation. In this paper, we present a novel specialization of RANSAC that extends the previous work. A unified framework is introduced to achieve simultaneously egomotion estimation, multiscale segmentation, and moving object detection in the RANSAC paradigm. The motivation of this work is to provide a robust real-time solution to the problem of egomotion estimation, segmentation, and moving object detection in highly dynamic environments. The idea is to augment the discriminative power of spatial and temporal appearances of objects by the spatiotemporal consistency. The objective is twofold. First, split mismerged segments and distinguish nonstationary objects from stationary objects by the spatial consistency. Second, merge oversegmented segments and differentiate moving objects from outlying objects by the temporal consistency. Moving objects of considerably different sizes, from pedestrians to trucks, can be properly segmented and correctly detected. We also show that the performance of egomotion estimation can be further improved by taking into account both stationary and moving object information. Our approach is extensively evaluated on challenging data sets and compared to the state of the art. The experiments also show that our approach serves as a general framework that works well with various planar range data. C 2011 Wiley Periodicals, Inc.

show abstract

Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment

Abstract: Abstract

Cited by 41 publications

References 9 publications

Modeling motion patterns of dynamic objects by IOHMM

Modeling motion patterns of dynamic objects by IOHMM

An Introduction to the Problem of Mapping in Dynamic Environments

Simultaneous egomotion estimation, segmentation, and moving object detection

Contact Info

Product

Resources

About