A Review of the Bayesian Occupancy Filter

Saval-Calvo, Marcelo; Medina-Valdés, Luis; Castillo-Secilla, José María; Cuenca-Asensi, Sergio; Martínez-Álvarez, Antonio; Villagrá, Jorge

doi:10.3390/s17020344

Cited by 31 publications

(27 citation statements)

References 38 publications

(92 reference statements)

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“…Many variations of the Bayesian Occupancy Filter have been developed over the years, mainly adding dynamic obstacles in the grid. Saval-Calvo et al [6] wrote a review of the different Bayesian Occupancy Filter frameworks. Finally, O'Callaghan et al [7] proposed a way to store the occupancy map without discretization, using gaussian process.…”

Section: Related Workmentioning

confidence: 99%

Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

Laconte

Debain

Chapuis

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In a context of autonomous robots, one of the most important tasks is to ensure the safety of the robot and its surrounding. The risk of navigation is usually said to be the probability of collision. This notion of risk is not well defined in the literature, especially when dealing with occupancy grids. The Bayesian occupancy grid is the most used method to deal with complex environments. However, this is not fitted to compute the risk along a path by its discrete nature. In this article, we present a new way to store the occupancy of the environment that allows the computation of risk along a given path. We then define the risk as the force of collision that would occur for a given obstacle. Using this framework, we are able to generate navigation paths ensuring the safety of the robot.

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Section: Related Workmentioning

confidence: 99%

Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

Laconte

Debain

Chapuis

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…But an accurate and wide range cell velocity estimation using these transition histograms of each cell requires enormous computing/memory consumptions and the discretization also leads to aliasing problems. However, this approach has been extended in various interesting approaches -a detailed review of the BOF including multiple extensions is given in [19].…”

Section: Related Workmentioning

confidence: 99%

“…which is discussed in more detail in the following. 1) Conflict Assignment: The update of the map as defined in (19) causes three conflicts…”

Section: A Prediction Of Dynamic Grid Mapmentioning

confidence: 99%

Grid-Based Environment Estimation Using Evidential Mapping and Particle Tracking

Steyer

Tanzmeister

Wollherr

2018

IEEE Trans. Intell. Veh.

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Modeling and estimating the current local environment by processing sensor measurement data is essential for intelligent vehicles. Static obstacles, dynamic objects, and free space have to be appropriately represented, classified, and filtered. Occupancy grids, known for mapping static environments, provide a common low-level representation using occupancy probabilities with an implicit data association through the discrete grid structure. Extending this idea toward dynamic environments with moving objects requires a static/dynamic classification of measured occupancy and a tracking of the dynamic state of grid cells. In this work, we propose a new dynamic grid mapping approach. An evidential representation using the Dempster-Shafer framework is used to model hypotheses for static occupancy, dynamic occupancy, free space, and their combined hypotheses. These hypotheses are consistently estimated and accumulated in a dynamic grid map by an adapted evidential filtering, allowing one to distinguish static and dynamic occupancy. The evidential grid mapping is combined with a low-level particle filter tracking that is used to estimate cell velocity distributions and predict dynamic occupancy of the grid map. Static occupancy is directly modeled in the grid map without requiring particles, increasing efficiency and improving the static/dynamic classification due to the persistent map accumulation. Experimental results with real sensor data show the effectiveness of the proposed approach in challenging scenarios with occlusions and dense traffic.

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“…Although some works by [ 18 , 19 , 20 ] are based on these methods, due to the amount of parameters tuning needed, it is usually a challenging task. Another popular approach is the Bayesian Occupancy Filter (BOF) [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Dynamic Obstacle Mapping (iDOMap)

Llamazares

Molinos

Ocaña

et al. 2020

Sensors

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The goal of this paper is to improve our previous Dynamic Obstacle Mapping (DOMap) system by means of improving the perception stage. The new system must deal with robots and people as dynamic obstacles using LIght Detection And Range (LIDAR) sensor in order to collect the surrounding information. Although robot movement can be easily tracked by an Extended Kalman Filter (EKF), people’s movement is more unpredictable and it might not be correctly linearized by an EKF. Therefore, to deal with a better estimation of both types of dynamic objects in the local map it is recommended to improve our previous work. The DOMap has been extended in three key points: first the LIDAR reflectivity remission is used to make more robust the matching in the optical flow of the detection stage, secondly static and a dynamic occlusion detectors have been proposed, and finally a tracking stage based on Particle Filter (PF) has been used to deal with robots and people as dynamic obstacles. Therefore, our new improved-DOMap (iDOMap) provides maps with information about occupancy and velocities of the surrounding dynamic obstacles (robots, people, etc.) in a more robust way and they are available to improve the following planning stage.

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A Review of the Bayesian Occupancy Filter

Cited by 31 publications

References 38 publications

Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

Lambda-Field: A Continuous Counterpart of the Bayesian Occupancy Grid for Risk Assessment

Grid-Based Environment Estimation Using Evidential Mapping and Particle Tracking

Improved Dynamic Obstacle Mapping (iDOMap)

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