Browsing by Author "Yandun, Francisco"

Now showing 1 - 4 of 4
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    Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Andrada, María Eduarda; Russell, David; Arévalo Ramírez, Tito; Kuang, Winnie; Kantor, George; Yandun, Francisco
    This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management.
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    Markov Chain Monte Carlo Parameter Estimation for Nonzero Slip Models of Wheeled Mobile Robots: A Skid-Steer Case Study
    (2021) Yandun, Francisco; Torres-Torriti, Miguel; Cheein, Fernando Auat
    An accurate modeling, simulation, and estimation of the wheel-terrain interaction and its effects on a robot movement plays a key role in control and navigation tasks, specially in constantly changing environments. We study the calibration of wheel slip models using Particle Markov Chain Monte Carlo methods to approximate the posterior distributions of their parameters. In contrast to classic identification approaches, considering the parameters as random variables allows to obtain a probability measure of the parameter estimations and subsequently propagate their uncertainty to wheel slip-related variables. Extensive simulation and experimental results showed that the proposed methodology can effectively get reliable posterior approximations from noisy sensor measurements in changing terrains. Validation tests also include the applicability assessment of the proposed methodology by comparing it with the integrated prediction error minimization methodology. Field results presented up to 66% of improvement in the robot motion prediction with the proposed calibration approach.
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    Overcoming the Loss of Performance in Unmanned Ground Vehicles Due to the Terrain Variability
    (2018) Prado, Javier; Yandun, Francisco; Torres Torriti, Miguel Attilio; Auat Cheein, Fernando
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    Point cloud-based estimation of effective payload volume for earthmoving loaders
    (2020) Guevara, Javier; Arévalo Ramírez, Tito; Yandun, Francisco; Torres Torriti, Miguel Attilio; Cheein, Fernando Auat