Sensor modeling for the Virtual Autonomous Navigation Environment

Goodin, Chris; Kala, Raju V.; Carrrillo, Alex; Liu, Linda Y.

doi:10.1109/icsens.2009.5398491

Cited by 19 publications

(11 citation statements)

References 6 publications

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“…Using fine grained triangular meshes combined with dithered multi-ray, ray traced simulations can approximate the behavior [3] but at substantial computational cost. Consider for example, the amount of memory and computing resources required to simulate 100 km 2 of wild grassland.…”

Section: Related Workmentioning

confidence: 99%

“…This problem can be overcome by using multi-sample raytracing methods to model the non-zero laser beam width and by building geometric models at the scale of the blades of grass [3]. This generates more realistic behavior in mixed-pixel scenarios and automated tools (e.g., http://www.bionatics.com/) can even simplify the construction process.…”

Section: Introductionmentioning

confidence: 99%

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Automatic data driven vegetation modeling for lidar simulation

Deschaud

Prasser

Dias

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Traditional lidar simulations render surface models to generate simulated range data. For objects with welldefined surfaces, this approach works well, and traditional 3D scene reconstruction algorithms can be employed to automatically generate the surface models. This approach breaks down, though, for many trees, tall grasses, and other objects with fine-scale geometry: surface models do not easily represent the geometry, and automated reconstruction from real data is difficult. In this paper, we introduce a new stochastic volumetric model that better captures the complexities of real lidar data of vegetation and is far better suited for automatic modeling of scenes from field collected lidar data. We also introduce several methods for automatic modeling and for simulating lidar data utilizing the new model. To measure the performance of the stochastic simulation we use histogram comparison metrics to quantify the differences between data produced by the real and simulated lidar. We evaluate our approach on a range of real world datasets and show improved fidelity for simulating geo-specific outdoor, vegetation scenes.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic data driven vegetation modeling for lidar simulation

Deschaud

Prasser

Dias

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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“…Core products of the VANE simulate geoenvironmental influences on sensor responses and vehicle dynamics to predict robotic behavior in a given environment 2 . VANE has been used for closed-loop system analysis for reconnaissance missions using autonomous routing, drive, and surveillance 3 ; pedestrian detection using Light Detection and Ranging (LIDAR); sensor error predictions for route planning 4 ; optimal camera placement by evaluating probability of detection versus look ahead distance 5 ; and currently applying advanced leader/follower autonomy to multiple tactical vehicle types that serve in convoys. The purpose of the near infrared (NIR) sensitivity analysis was to set up VANE simulations to measure the environmental effect on performance of a camera system in detecting an active fiducial marker.…”

Section: Introductionmentioning

confidence: 99%

“…The Virtual Autonomous Navigation Environment (VANE) is a high-fidelity, physics-based simulation software that produces realistic simulated sensor output for use in the development and testing of manned and unmanned ground vehicles 1 . The VANE integrates simulations of sensors, vehicle dynamics, terrain mechanics, and environmental conditions and utilizes physics-based sensor, thermal, vehicle-terrain interaction, groundwater, and reflectance models.…”

Section: Introductionmentioning

confidence: 99%

NIR sensitivity analysis with the VANE

2016

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Near infrared (NIR) cameras, with peak sensitivity around 905-nm wavelengths, are increasingly used in object detection applications such as pedestrian detection, occupant detection in vehicles, and vehicle detection. In this work, we present the results of simulated sensitivity analysis for object detection with NIR cameras. The analysis was conducted using high performance computing (HPC) to determine the environmental effects on object detection in different terrains and environmental conditions. The Virtual Autonomous Navigation Environment (VANE) was used to simulate highresolution models for environment, terrain, vehicles, and sensors.In the experiment, an active fiducial marker was attached to the rear bumper of a vehicle. The camera was mounted on a following vehicle that trailed at varying standoff distances. Three different terrain conditions (rural, urban, and forest), two environmental conditions (clear and hazy), three different times of day (morning, noon, and evening), and six different standoff distances were used to perform the sensor sensitivity analysis. The NIR camera that was used for the simulation is the DMK firewire monochrome on a pan-tilt motor.Standoff distance was varied along with environment and environmental conditions to determine the critical failure points for the sensor. Feature matching was used to detect the markers in each frame of the simulation, and the percentage of frames in which one of the markers was detected was recorded. The standoff distance produced the biggest impact on the performance of the camera system, while the camera system was not sensitive to environment conditions.

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“…Since these simulators mainly focus on simulating or emulating sensor node architecture such as memory, batteries, wireless transmission and protocols, they are not interested in simulating the behavior of sensing activity. Although some researches such as [7] focus on modeling and simulation of sensors, the emulated outputs are not the capture of "real world" but are synthetic. Therefore, it is not possible to examine how sensor capability such as sensing accuracy and sensor types affect the performance of target systems.…”

Section: Related Workmentioning

confidence: 99%

HumanS: A Human Mobility Sensing Simulator

Kanaya

Hiromori

Yamaguchi

et al. 2012

2012 5th International Conference on New Technologies, Mobility and Security (NTMS)

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Abstract-In this paper, we propose a human mobility sensing system simulator called HumanS. HumanS is a multi-agent simulator that works with geographic information system (GIS). It models realistic movement of pedestrians and behavior of sensors that capture the mobility. Developers can evaluate performance of such sensing systems that detect location of people for pedestrian navigation, purchasing behavior analysis and so on under realistic model of environment. The simulator is designed based on GIS databases that store and manage the scanning data by appropriately tagging their time and location. The capability of the HumanS simulator has been validated by a scenario where accuracy of a pedestrian flow sensing system is tested with different settings of sensor types and location in Osaka downtown.

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Sensor modeling for the Virtual Autonomous Navigation Environment

Cited by 19 publications

References 6 publications

Automatic data driven vegetation modeling for lidar simulation

Automatic data driven vegetation modeling for lidar simulation

NIR sensitivity analysis with the VANE

HumanS: A Human Mobility Sensing Simulator

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