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A novel LiDAR-detectable plate-like hollow black titanium dioxide (HbTiO 2) is developed by recycling silicon sludge generated from silicon wafer sawing. By employing TiCl 4 sol-gel synthesis, hydrofluoric acid etching, and NaBH 4 reduction, the hollow-structured black TiO 2 is successfully synthesized. Plate-like HbTiO 2 readily mixed with hydrophilic varnish, owing to its inherent hydrophilic properties. With monolayer coating, HbTiO 2 -based paints exhibit the blackness (L * = 17.63) comparable to that of commercial black paints, indicating that NaBH 4 successfully changed the color of TiO 2 from white to black. In addition to its blackness, HbTiO 2 exhibits a superior near-infrared (NIR) reflectance of ca. 26.8 R % at 905 nm, making it suitable for integration with the LiDAR systems used in autonomous vehicles. This high NIR reflectance ensures that HbTiO 2 can effectively interact with the LiDAR sensors, attributing to the hollow structures and effective light reflection mechanism. Furthermore, the use of recycled silicon sludge not only offers a cost-effective alternative to traditional template materials but also promotes environmental sustainability by reducing solid waste. Our findings demonstrate the potential of HbTiO 2 as an innovative and practical LiDAR-detectable black pigment, paving the way for advanced applications in autonomous vehicle technologies. [Display omitted] • Hollow black TiO 2 (HbTiO 2) is synthesized as NIR-reflective material. • Recycling silicon sludge as template is nominated first time. • With creation of hollow structure, the highest NIR reflection is measured. • Recycling silicon sludge promotes zero-waste and practical alternative templates. • In monolayer, HbTiO 2 -coated object successfully recognized by the LiDAR sensors. [ABSTRACT FROM AUTHOR]

Safe collaboration between a robotic and human agent is an important challenge yet to be fully overcome in a manufacturing set-up. Existing strategies, including safety zoning are sub-optimal since they seldom fully exploit the capabilities of the collaborative robot (for repetitive tasks) and highly cognitive tasks (best suited for the operator). The recently released ISO 15066 standard for collaborative robots proposes varying safeguards, including force, speed and distance limiting functions. The latter is particularly attractive as it allows the robotic agent to adapt its operating behaviour in proximity of the operator and in instances likely to lead to safety hazards. This paper discusses strategies explored for implementing dynamic zoning in shared workspaces, considering the input speed/force of the robot as dependent on the distance between human and robot. Two main strategies were modelled, for implementing zoning. The first strategy explored integrating a LiDAR sensor, and utilising LiDAR data to dynamically map the separation distances between the operator and robotic agent. The second strategy explores an experimental setup utilising the Microsoft Kinect V2 sensor for capturing 3D point clouds, and in turn, detecting objects/agents and the proximity distance. In both instances, objects/agents were detected up to a separation distance threshold, considering error sensitivity below values of 0.1 meters. Both use cases were demonstrated using a Yumi robot and form the basis of future work towards dynamic workspace zoning. [ABSTRACT FROM AUTHOR]

[Display omitted] • Black double-shell hollow nanoparticles (BDS-HNPs) is prepared as LiDAR-reflective materials. • Strategies of multiple interfaces and internal white shell result as a superb NIR reflectance. • With the hydrophilic nature, BDS-HNPs are easily formulated as eco-friendly hydrophilic paint. • BDS-HNPs are dually function as NIR reflective and black color exhibiting layer in monolayer. • Three types of LiDAR sensors are employed for practical recognition of BDS-HNPs-painted object. Novel LiDAR-detectable black double-shell hollow nanoparticles (BDS-HNPs) with internal white shell are successfully utilized as materials for autonomous vehicle paint for the first time. These BDS-HNPs are carefully designed to achieve excellent near-infrared (NIR) reflectance, blackness, hydrophilicity, and applicability as monolayer coatings. An emphasis is placed on the NIR reflectance by forming double-shell hollow morphologies embracing the internal white shell and multiple interfaces within the nanoparticles. Accordingly, the BDS-HNPs exhibit NIR reflectance of ca. 33.2, 36.9, and 40.9 R % at wavelengths of 793, 850, and 905 nm, respectively, comparable to NIR reflectance of the commercially available NIR-reflective bilayer dark-tone coating. For the practical LiDAR visualization, BDS-HNPs mixed with hydrophilic varnish are spray-coated onto the various objects. As a result, the BDS-HNPs-painted objects are clearly recognized by three different types of LiDAR sensors (robot, rotating, and MEMs mirror) under various conditions of inside and outside. These results clearly demonstrate the great potential of BDS-HNPs as a new type of LiDAR-detectable black material for future autonomous driving environments. [ABSTRACT FROM AUTHOR]

Handling and interpreting sparse 3D point clouds, especially from mmWave radar, presents unique challenges due to the inherent data sparsity and the vast domain difference compared to denser point clouds like those from LiDAR. In this paper, we introduce a novel cascaded generative adversarial network (GAN) approach to bridge this domain gap. The core principle is to progressively refine the radar-based point cloud through a series of GANs, each targeting a higher resolution. By leveraging multi-level features and a hybrid loss function that combines adversarial, geometric, and consistency components, our method ensures a smooth transition from the sparse radar representation to a high-resolution LiDAR-like point cloud. Our cascaded approach operates at a patch level, and the integrated loss function ensures that the generated points not only resemble the target domain but also maintain geometric and structural fidelity. Real-life dataset consisting mostly of moving pedestrians were collected using a system made of Radar, LiDAR, and RGB Camera. Through an extensive experiment on the collected real-world pedestrian dataset, we validate the efficacy of our approach. Inference from the network indicates that our method can upsample mmWave radar point clouds with enhanced density, uniformity, and closer alignment to the ground truth LiDAR point clouds, which is the first of its kind network to do so. • Novel radar point cloud upsampling method using adversarial cascade approach. • Modification of CycleGAN for handling point clouds. • Dynamic batching to handle varying point clouds. • Enhanced radar-based point cloud method for improved people detection. [ABSTRACT FROM AUTHOR]

• A novel real-time counting system is developed for monitoring high pedestrian flows. • The proposed system uses distance measurements from a two-dimensional LiDAR sensor. • Clustering algorithm is developed to count pedestrians and identify their direction. • The results show that the system accurately counts more than 97% of the pedestrians. • The accuracy of the proposed system is higher than the traditional technologies. Automated monitoring of pedestrians on non-motorized facilities with high pedestrian flows is challenging. Several automated sensor solutions are commercially available that have been evaluated in the literature including traditional point-based sensors, such as inductive loop detectors for bicycles and infrared sensors for pedestrians. More recently, image-based systems, based on video cameras or thermal video cameras, have been developed. Despite the various options, some key limitations of existing solutions exist, in particular, the lack of low-cost solutions using embedded systems capable of performing in real-time under high volume (flow) conditions. This work aims at developing and evaluating the performance of a novel, real-time counting system, developed for environments with high pedestrian flows. The proposed system is based on emerging LiDAR (Light Detection and Ranging) technology. As an input, the system uses the distance measurements from a two-dimensional LiDAR sensor with a set of distinct laser channels and a given angular resolution between each channel. The developed system processes those measurements using a clustering algorithm to detect, count, and identify the direction of travel of each pedestrian. The system's performance is evaluated by comparing its directional counting outputs with manual counts (ground truth) using disaggregate and aggregate (15-minutes interval) counts at two different monitoring locations. The results demonstrate that the system accurately counts more than 97% of the pedestrians at the disaggregate level, with a false direction detection rate of 1.1%. The over-counting error is 0.7% and the under-counting errors are 1.3% and 2.7% for the two selected sites. At the aggregate level (15-minutes interval), the average absolute percentage deviations (AAPDs) are 1.6% and 4.3% while the weighted AAPDs are 1.5% and 3.5% for the first and second sites, respectively. The accuracy of the proposed system is higher than the traditional technologies used for the same purpose. [ABSTRACT FROM AUTHOR]

Monte Carlo localization methods deploy a particle filter to resolve a hidden Markov process based on recursive Bayesian estimation, which approximates the internal states of a dynamic system given observation data. When the observed data are corrupted by outliers, the particle filter's performance may deteriorate, preventing the algorithm from accurately computing dynamic system states such as a robot's position, which in turn reduces the accuracy of the localization and navigation. In this paper, the notion of information entropy is used to identify outliers. Then, a probability-based approach is used to remove the discovered outliers. In addition, a new mutation process is added to the localization algorithm to exploit the posterior probability density function in order to actively detect the high-likelihood region. The goal of incorporating the mutation operator into this method is to solve the problem of algorithm impoverishment which is due to insufficient representation of the complete probability density function. Simulation experiments are used to confirm the effectiveness of the proposed techniques. They also are employed to predict the remaining viability of a lithium-ion battery. Furthermore, in an experimental study, the modified Monte Carlo localization algorithm was applied to a mobile robot to demonstrate the local planner's improved accuracy. The test results indicate that developed techniques are capable of effectively capturing the dynamic behavior of a system and accurately tracking its characteristics. [ABSTRACT FROM AUTHOR]

Abstract The measurement of total catch on-board a fishing vessel is generally a very complex process, especially to scientific observers. In this context, hauls of large volume, intricate fishing operations and limited access to the capture increase this problem. In this paper, we propose a methodology to address the estimation of catch volume in codends of a crustacean fishery through LiDAR (light and radar) technology. A sensor was used to acquire a three-dimensional representation of an object located at a fixed distance from the device, thereby simulating a fishing codend. Then, a convex-hull algorithm was applied to this representation to obtain an estimation of its volume. Additionally, to obtain further insights, an experimental laboratory setup was used to emulate the volume estimation of catches on a fishing vessel. The dataset acquired by the system was subsequently analyzed to study the percentage errors associated with the estimation process and to test whether the selected variables are significant. The results indicate that there is considerable uncertainty related to the volume estimation, but it can be addressed using a statistical model. This work constitutes the first attempt to provide a methodology to estimate the catch volume of a codend in a Chilean fishery by generating new measurement alternatives for fishery monitoring programs, enforcement and management institutions, as well as the fishing industry. Highlights • A new methodology to estimate catch volume in codends. • The method considers a LiDAR sensor to acquire the 3D information. • A convex-hull algorithm generates volume estimates. • The estimation uncertainty is addressed using a statistical model. • Our method has several advantages with respect to the observers, which is used at present. [ABSTRACT FROM AUTHOR]

Efficient information management in orchard characterization leads to more efficient agricultural processes. In this brief, a set of computational geometry methods are presented and evaluated for orchard characterization; in particular, for the estimation of canopy volume and shape in groves and orchards using a LiDAR (Light Detection And Ranging) sensor mounted on an agricultural service unit. The proposed approaches were evaluated and validated in the field, showing they are convergent in the estimation process and that they are able to estimate the crown volume for fully scanned canopies in real time; for partially observed tree crowns, accuracy decreases up to 30% (the worst case). The latter is the major contribution of this brief since it implies that the automated service unit does not need to cover all alley-ways for an accurate modeling of the orchard, thus saving valuable resources. [ABSTRACT FROM AUTHOR]

• To address the density imbalanced problem in point clouds, we propose a novel spatial information enhancement module (SIE) to predict the dense shapes of point sets in candidate boxes, and learn the structure information to improve the ability of feature representation. • We present a hybrid-paradigm region proposal network (HP-RPN) for more effective multi-scale feature extraction and high-recall proposal generation. • With the structure information as guidance, our elaborately designed SIENet achieves the state-of-the-art performance of 3D object detection on the KITTI benchmark. • The encouraging experimental results also demonstrate the outstanding improvement in far-range object detection. LiDAR-based 3D object detection pushes forward an immense influence on autonomous vehicles. Due to the limitation of the intrinsic properties of LiDAR, fewer points are collected at the objects farther away from the sensor. This imbalanced density of point clouds degrades the detection accuracy but is generally neglected by previous works. To address the challenge, we propose a novel two-stage 3D object detection framework, named SIENet. Specifically, we design the Spatial Information Enhancement (SIE) module to predict the spatial shapes of the foreground points within proposals, and extract the structure information to learn the representative features for further box refinement. The predicted spatial shapes are complete and dense point sets, thus the extracted structure information contains more semantic representation. Besides, we design the Hybrid-Paradigm Region Proposal Network (HP-RPN) which includes multiple branches to learn discriminate features and generate accurate proposals for the SIE module. Extensive experiments on the KITTI 3D object detection benchmark show that our elaborately designed SIENet outperforms the state-of-the-art methods by a large margin. Codes will be publicly available at https://github.com/Liz66666/SIENet. [ABSTRACT FROM AUTHOR]

Abstract: Discussions in recent decades about dosage models for applying plant protection products in orchards have failed to reach a compromise solution. Furthermore, canopies are spatially variable, and a uniform dose may not be adequate for the entire orchard. Spraying at an adequate volume application rate on a site-specific basis would help reduce the amount of agrochemicals used in the framework of precision horticulture and precision fructiculture. An orchard sprayer prototype running a variable-rate algorithm to adapt the volume application rate to the canopy volume in orchards on a real-time and continuous basis was designed, implemented, and validated. An equivalent prototype was designed for vineyards and described in a companion paper (‘Variable rate sprayer. Part 2 – Vineyard prototype: Design, implementation and validation’). The orchard prototype was divided into three parts: the canopy characterization system (using a LiDAR sensor), the controller executing a variable-rate algorithm, and the actuators. The controller determines the intended flow rate by using an application coefficient (required liquid volume per unit canopy volume) to convert canopy volume into a flow rate. The sprayed flow rates are adjusted via electromagnetic variable-rate valves. The goal of the prototype was to keep the actual application coefficients as close as possible to the objective. Strong relationships were observed between the intended and the sprayed flow rates (R 2 =0.935) and between the canopy cross-sectional areas and the sprayed flow rates (R 2 =0.926). In addition, when spraying in variable-rate mode, the prototype achieved significantly closer application coefficient values to the objective than those obtained in conventional spraying application mode. [Copyright &y& Elsevier]

This paper proposes a novel system to inspect the LiDAR sensor misalignment on a vehicle during the sensor assembly process in the autonomous vehicle productions. The inspection system is primarily designed for deployment in the factory-level assembly process for commercial vehicles with ADAS and autonomous driving functions. To obtain sub-degree and millimeter accuracy levels of the LiDAR sensor alignment or the extrinsic pose from the vehicle body, this study proposed a new concept of LiDAR sensor extrinsic calibration method using a target board with embedded photodetector(PD) arrays, named the PD–target system. Furthermore, the proposed system requires only the simple design of the target board at the fixed pose in a close range to be readily applicable in the automobile manufacturing environment. From the estimation of the extrinsic pose of the LiDAR sensor from the target board, the LiDAR sensor alignment with respect to the vehicle body can be easily derived in the controlled factory environment, where positions of both the target board and the vehicle are assumed to be known. The experimental evaluation of the proposed system on low-resolution LiDAR sensor showed that the LiDAR sensor offset pose can be estimated within 0.1 degree and 3 mm levels of precision. The high accuracy and simplicity of the proposed calibration system make it practical for production of reliable and safe autonomous transportation. • Inspect LiDAR sensor misalignment on a vehicle during the sensor assembly process. • Designed for deployment in the factory-level assembly for commercial vehicles. • LiDAR extrinsic calibration using a target board with embedded photodetector arrays. • LiDAR sensor pose estimated within 0.1 degree and 3 mm of precision. • Requires a single target at static pose, applicable to manufacturing automobiles. [ABSTRACT FROM AUTHOR]

Zoometric measurements have a potential value in differentialing between individuals and within populations. The measurement of body size in horses and livestock plays a significant role in functional longevity, production, and reproductive performance and health. In this context, the measurements obtained without contact by detection systems and visualized by computers could represent a great advance over conventional measurements that are tedious, time consuming and stressful for the animals. This study presents a new approach to taking zoometric measurements of an animal's body based on digital three-dimensional modelling. The capture of the data series was carried out by a LiDAR sensor. The 16 laser beams of the sensor were able to fully scan a horse, performing a 3D reconstruction of the horse's side, through which body measurements were obtained. Five Pura Raza Española horses (PRE) (3 stallions and 2 mares) with ages ranging between 5 and 18 years old were scanned. The PRE is the most recognized native Spanish horse population for its census (national and international), cultural and socioeconomic importance. For each horse, 17 zoometric measurements (linear and angular) were taken both manually and using the LiDAR-based system to check the usefullness of this non-invasive technology in obtaining quick livestock measurements while causing minimal stress to the animals. Of the 17 zoometric measurements obtained manually and with the sensor, 10 (58.82%) had a mean relative error that ranged between > 0 and < 10; 5 (29.41%) had an error that ranged ≥ 10 and < 20; and two (As and ACr) had an error ≥ 20 (11.76%). A total of 82.5% of the traits studied had an accuracy (v2) lower than 5%. Therefore, although this approach could still be improved, it verifies the viability of noncontact measurements of large livestock. [ABSTRACT FROM AUTHOR]