The Virtual Autonomous Navigation Environment: High Fidelity Simulations of Sensor, Environment, and Terramechanics for Robotics

Goodin, C.; George, T.; Cummins, Christopher L.; Durst, Phillip J.; Gates, Burhman Q.; McKinley, George B.

doi:10.1061/9780784412190.153

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The second category of robotic simulator could be called "Robotic Test Environments" (RTE), in keeping with the above nomenclature. Simulators such as the Virtual Autonomous Navigation Environment (VANE) [34], MODSIM [35], Robotic Interactive Visualization Experimentation Technology (RIVET) [36], the Autonomous Navigation Virtual Environment Laboratory (ANVEL) [37], and the CARLA [26] fall into this category. RTE are typically used to evaluate the performance of a robotic system in a realistic operational setting-necessitating a higher degree of realism in the robot-environment interaction physics.…”

Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

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Machine learning techniques have accelerated the development of autonomous navigation algorithms in recent years, especially algorithms for on-road autonomous navigation. However, off-road navigation in unstructured environments continues to challenge autonomous ground vehicles. Many off-road navigation systems rely on LIDAR to sense and classify the environment, but LIDAR sensors often fail to distinguish navigable vegetation from non-navigable solid obstacles. While other areas of autonomy have benefited from the use of simulation, there has not been a real-time LIDAR simulator that accounted for LIDAR–vegetation interaction. In this work, we outline the development of a real-time, physics-based LIDAR simulator for densely vegetated environments that can be used in the development of LIDAR processing algorithms for off-road autonomous navigation. We present a multi-step qualitative validation of the simulator, which includes the development of an improved statistical model for the range distribution of LIDAR returns in grass. As a demonstration of the simulator’s capability, we show an example of the simulator being used to evaluate autonomous navigation through vegetation. The results demonstrate the potential for using the simulation in the development and testing of algorithms for autonomous off-road navigation.

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Section: Discussionmentioning

confidence: 99%

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

et al. 2018

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“…Three different terrain conditions were evaluated for the NIR sensitivity analysis using geo-specific and geo-typical scenes. Geo-specific scenes are built with ground truth attributions for reflectance, soil strength, and scene geometry, while geo-typical scenes are built using a library of materials 7 . The three different terrain conditions are shown in Figure 3.…”

Section: Testing Setup and Environmental Conditionmentioning

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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“…Developing accurate and realistic scenes is often expensive and time consuming. 3 Collecting ground-truth data, developing digitized versions of that data, and relying on artists to turn that data into digital assets is labor intensive and prone to errors. Given the level of effort required to create digital assets, the question often arises -how "good" do digital models need to be for supporting AGV simulations?…”

Section: Introductionmentioning

confidence: 99%

Fidelity requirements for simulating sensor performance in autonomous ground vehicles

Goodin

Carruth

Dabbiru

et al. 2023

Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications

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Simulation has become an important enabler in the development and testing of autonomous ground vehicles (AGV), with simulation being used both to generate training data for AI/ML-based segmentation and classification algorithms and to enable in-the-loop testing of the AGV systems that use those algorithms. Furthermore, digital twins of physical test areas provide a safe, repeatable way to conduct critical safety and performance testing of these AI/ML algorithms and their performance on AGV systems. For both these digital twins and the sensor models that use them to generate synthetic data, it is important to understand the relationship between the fidelity of the scene/model and the accuracy of the resulting synthetic sensor data. This work presents a quantitative evaluation of the relationship between digital scene fidelity, sensor model fidelity, and the quality of the resulting synthetic sensor data, with a focus on camera data typically used on AGV to enable autonomous navigation.

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The Virtual Autonomous Navigation Environment: High Fidelity Simulations of Sensor, Environment, and Terramechanics for Robotics

Cited by 5 publications

References 7 publications

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

Enabling Off-Road Autonomous Navigation-Simulation of LIDAR in Dense Vegetation

NIR sensitivity analysis with the VANE

Fidelity requirements for simulating sensor performance in autonomous ground vehicles

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