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1. Simple Camera-to-2D-LiDAR Calibration Method for General Use

   Palmer, A.; Peterson, C.; Blankenburg, J.; Feil-Seifer, D.; Nicolescu, M. (October 2020, ISVC'20: 15TH INTERNATIONAL SYMPOSIUM ON VISUAL COMPUTING)

   null (Ed.)

   As systems that utilize computer vision move into the public domain, methods of calibration need to become easier to use. Though multi-plane LiDAR systems have proven to be useful for vehicles and large robotic platforms, many smaller platforms and low cost solutions still require 2D LiDAR combined with RGB cameras. Current methods of calibrating these sensors make assumptions about camera and laser placement and/or require complex calibration routines. In this paper we propose a new method of feature correspondence in the two sensors and an optimization method capable of calibration target with unknown lengths in its geometry. Our system is designed with an inexperienced layperson as the intended user, which has lead us to remove as many assumptions about both the target and laser as possible. We show that our system is capable of calibrating the 2-sensor system from a single sample in configurations other methods are unable to handle. 

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2. Autonomous in situ calibration of ion‐sensitive field effect transistor pH sensors

   https://doi.org/10.1002/lom3.10410  

   Bresnahan, Philip J.; Takeshita, Yuichiro; Wirth, Taylor; Martz, Todd R.; Cyronak, Tyler; Albright, Rebecca; Wolfe, Kennedy; Warren, Joseph K.; Mertz, Keaton (January 2021, Limnology and Oceanography: Methods)

   Abstract Ion‐sensitive field effect transistor‐based pH sensors have been shown to perform well in high frequency and long‐term ocean sampling regimes. The Honeywell Durafet is widely used due to its stability, fast response, and characterization over a large range of oceanic conditions. However, potentiometric pH monitoring is inherently complicated by the fact that the sensors require careful calibration. Offsets in calibration coefficients have been observed when comparing laboratory to field‐based calibrations and prior work has led to the recommendation that an in situ calibration be performed based on comparison to discrete samples. Here, we describe our work toward a self‐calibration apparatus integrated into a SeapHOx pH, dissolved oxygen, and CTD sensor package. This Self‐Calibrating SeapHOx is capable of autonomously recording calibration values from a high quality, traceable, primary reference standard: equimolar tris buffer. The Self‐Calibrating SeapHOx's functionality was demonstrated in a 6‐d test in a seawater tank at Scripps Institution of Oceanography (La Jolla, California, U.S.A.) and was successfully deployed for 2 weeks on a shallow, coral reef flat (Lizard Island, Australia). During the latter deployment, the tris‐based self‐calibration using 15 on‐board samples exhibited superior reproducibility to the standard spectrophotometric pH‐based calibration using > 100 discrete samples. Standard deviations of calibration pH using tris ranged from 0.002 to 0.005 whereas they ranged from 0.006 to 0.009 for the standard spectrophotometric pH‐based method; the two independent calibration methods resulted in a mean pH difference of 0.008. We anticipate that the Self‐Calibrating SeapHOx will be capable of autonomously providing climate quality pH data, directly linked to a primary seawater pH standard, and with improvements over standard calibration techniques. 

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3. Handheld lidar sensors can accurately measure aboveground biomass

   https://doi.org/10.1002/ecs2.70232  

   Atkins, David_H; Blackburn, Ryan_C; Laughlin, Daniel_C; Moore, Margaret_M; Sánchez_Meador, Andrew_J (June 2025, Ecosphere)

   Abstract Many recent studies have explored remote sensing approaches to facilitate non‐destructive sampling of aboveground biomass (AGB). Lidar platforms (e.g., iPhone and iPad PRO models) have recently made remote sensing technologies widely available and present an alternative to traditional approaches for estimating AGB. Lidar approaches can be completed within a fraction of the time required by many analog methods. However, it is unknown if handheld sensors are capable of accurately predicting AGB or how different modeling techniques affect prediction accuracy. Here, we collected AGB from 0.25‐m²plots (N = 45) from three sites along an elevational gradient within rangelands surrounding Flagstaff, Arizona, USA. Each plot was scanned with a mobile laser scanner (MLS) and iPad before plants were clipped, dried, and weighed. We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field‐based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. We found that models developed from MLS and iPad data were equally capable of predicting AGB (NRMSE 26.6% and 29.3%, respectively) regardless of the variable subsets considered. We also found that stepwise regression regularly resulted in the lowest NRMSE. Structural variables were consistently selected during each modeling approach, while spectral variables were rarely included. Field‐based variables were important in linear regression models but were not included after variable selection within random forest models. These findings support the notion that remote sensing techniques offer a valid alternative to analog field‐based data collection methods. Together, our results demonstrate that data collected using a more widely available platform will perform similarly to a more costly option and outline a workflow for modeling AGB using remote sensing systems alone. 

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4. Active Laser-Camera Scanning for High-Precision Fruit Localization in Robotic Harvesting: System Design and Calibration

   https://doi.org/10.3390/horticulturae10010040  

   Zhang, Kaixiang; Chu, Pengyu; Lammers, Kyle; Li, Zhaojian; Lu, Renfu (January 2024, Horticulturae)

   Robust and effective fruit detection and localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving fruit detection, less emphasis has been placed on the fruit localization aspect, which is a crucial yet challenging task due to limited depth accuracy from existing sensor measurements in the natural orchard environment with variable lighting conditions and foliage/branch occlusions. In this paper, we present the system design and calibration of an Active LAser-Camera Scanner (ALACS), a novel perception module for robust and high-precision fruit localization. The hardware of the ALACS mainly consists of a red line laser, an RGB camera, and a linear motion slide, which are seamlessly integrated into an active scanning scheme where a dynamic-targeting laser-triangulation principle is employed. A high-fidelity extrinsic model is developed to pair the laser illumination and the RGB camera, enabling precise depth computation when the target is captured by both sensors. A random sample consensus-based robust calibration scheme is then designed to calibrate the model parameters based on collected data. Comprehensive evaluations are conducted to validate the system model and calibration scheme. The results show that the proposed calibration method can detect and remove data outliers to achieve robust parameter computation, and the calibrated ALACS system is able to achieve high-precision localization with the maximum depth measurement error being less than 4 mm at distance ranging from 0.6 to 1.2 m. 

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5. A unified calibration framework for 21 cm cosmology

   https://doi.org/10.1093/mnras/stab647  

   Byrne, Ruby; Morales, Miguel F; Hazelton, Bryna J; Wilensky, Michael (March 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT Calibration precision is currently a limiting systematic in 21 cm cosmology experiments. While there are innumerable calibration approaches, most can be categorized as either ‘sky-based,’ relying on an extremely accurate model of astronomical foreground emission, or ‘redundant,’ requiring a precisely regular array with near-identical antenna response patterns. Both of these classes of calibration are inflexible to the realities of interferometric measurement. In practice, errors in the foreground model, antenna position offsets, and beam response inhomogeneities degrade calibration performance and contaminate the cosmological signal. Here, we show that sky-based and redundant calibration can be unified into a highly general and physically motivated calibration framework based on a Bayesian statistical formalism. Our new framework includes sky-based and redundant calibration as special cases but can additionally support relaxing the rigid assumptions implicit in those approaches. We present simulation results demonstrating that, in a simple case, working in an intermediate regime between sky-based and redundant calibration improves calibration performance. Our framework is highly general and encompasses novel calibration approaches including techniques for calibrating compact non-redundant arrays, calibrating to incomplete sky models, and constraining calibration solutions across frequency. 

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