Customizable Inverse Sensor Model for Bayesian and Dempster-Shafer Occupancy Grid Frameworks

Porębski, Jakub

doi:10.1007/978-3-030-50936-1_102

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…Grid map size was selected as

m with a cell resolution of

m based on previous highway grid parameter reviews [ 25 ]. The sensor models were designed using dual architecture, as presented in [ 27 ]. The occupancy grid vehicle was moved to the rear border to ensure a minimum of 100 m of mapping in front of the vehicle.…”

Section: Methodsmentioning

confidence: 99%

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Performance Evaluation of the Highway Radar Occupancy Grid

Porębski

Kogut²

2021

Sensors

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The quality of environmental perception is crucial for automated vehicle capabilities. In order to ensure the required accuracy, the occupancy grid mapping algorithm is often utilised to fuse data from multiple sensors. This paper focuses on the radar-based occupancy grid for highway applications and describes how to measure effectively the quality of the occupancy map. The evaluation was performed using the novel grid pole-like object analysis method. The proposed assessment is versatile and can be applied without detailed ground truth information. The evaluation was tested with a simulation and real vehicle experiments on the highway.

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“…Grid map size was selected as

m with a cell resolution of

Section: Methodsmentioning

confidence: 99%

“…This article implements the triangle ray casting method presented in prior research [ 27 ], which solves the problem of artefacts by using a wider area for updates. Every cell in the triangle ray is filled with the same value, thereby forming a uniform free space probability distribution.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of the Highway Radar Occupancy Grid

Porębski

Kogut²

2021

Sensors

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“…They treat ground detections as sources of evidence for the hypothesis free and apply backward extrapolation to deduce free space evidence to neighboring grid cells. Porębski [12] presented a customizable inverse sensor model to calculate occupancy grid maps. In order to be able to compute accurate probabilities in each grid cell, they proposed a cell selection process and apply either a Gaussian or an exponential distribution to compute the inverse sensor model.…”

Section: Related Workmentioning

confidence: 99%

“…a measurement element m ∈ M is a detection coordinate indicating the presence of a reflecting surface with attached semantic label ω m . Note that other information such as LiDAR intensities or radio detection and ranging (RaDAR) Doppler measurements are omitted here as they are not considered in Equation (12). Point set measurements may be obtained from range sensors such as RaDARs.…”

Section: A Grid Mapping With Point Setsmentioning

confidence: 99%

Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles

Richter¹,

Bieder²,

Wirges³

et al. 2022

Preprint

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We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDAR and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDAR and stereo camera data recorded in real traffic scenarios. Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.

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