Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera

Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-flight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides a dense angular resolution. In this paper, Gaussian-based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. A probabilistic Kalman-filter framework is provided to track the moving objects, estimate their velocities and simultaneously correct the point clouds distortions. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method. The complete working framework is open-sourced (https://github.com/ISEE-Technology/lidar-with-velocity) to accelerate the adoption of the emerging lidars

Analysis of vibration characteristics of rotating parallel flexible manipulator considering joint elastic constraints

The bolt joint is the key component connecting the rigid moving base body and the flexible manipulator. The dynamic characteristics of the flexible manipulator under the elastic constraint of the joint are analyzed, and the action mechanism of the elastic constraint of the bolt joint on the frequency and vibration mode is revealed. Considering the effects of line constraint and torsion constraint, the elastic constraint model of the joint is established. Based on the principle of virtual work, the boundary constraints of the joint end and the free end are established, and the analytical equation of frequency and the expression of vibration mode function are derived. The first three frequencies and vibration mode characteristics of the flexible manipulator under elastic constraints are analyzed numerically. The sensitivity method is used to analyze the effect of linear constraints and torsional constraints on the frequency, and the elastic constraint region is established to characterize the functional relationship between the binding stiffness and the natural frequency. It is found that under elastic constraints, the influence of torsional stiffness of bolt joint is mainly concentrated in the low-order modal frequency, while the linear stiffness has a great influence on each order modal frequency of the manipulator; With the decrease of elastic constraint stiffness, its influence on modal shapes gradually increases, especially on high-order modal shapes. The research results prove the internal mechanism of the influence of elastic constraints on vibration characteristics, which provide a theoretical basis for improving the dynamic characteristics of flexible manipulator

Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

Funding Information: This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with Grant No. ST/N006852/1 . Xi’an Tongfei Aviation Technology Co., Ltd was also acknowledged for their professional support in flying UAV for data collection.Peer reviewe

Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring

Agriculture is facing severe challenges from crop stresses, threatening its sustainable development and food security. This work exploits aerial visual perception for yellow rust disease monitoring, which seamlessly integrates state-of-the-art techniques and algorithms including UAV sensing, multispectral imaging, vegetation segmentation and deep learning U-Net. A field experiment is designed by infecting winter wheat with yellow rust inoculum, on top of which multispectral aerial images are captured by DJI Matrice 100 equipped with RedEdge camera. After image calibration and stitching, multispectral orthomosaic is labelled for system evaluation by inspecting high-resolution RGB images taken by Parrot Anafi Drone. The merits of the developed framework drawing spectral-spatial information concurrently are demonstrated by showing improved performance over purely spectral based classifier by the classical random forest algorithm. Moreover, various network input band combinations are tested including three RGB bands and five selected spectral vegetation indices by Sequential Forward Selection strategy of Wrapper algorithm

Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales

A novel oligomer containing DOPO and ferrocene groups: Synthesis, characterization, and its application in fire retardant epoxy resin

A novel oligomer (PFDCHQ) based on 9,10-dihydro-9-oxa-10-phosphaphenanthrene −10-oxide (DOPO) and ferrocene groups was synthesized successfully, aiming at improving the flame retardant efficiency of diglycidyl ether of bisphenol A epoxy resin (DGEBA). FTIR, 1H NMR and 31P NMR were used to confirm the chemical structure of PFDCHQ. The high char yields of 60.3 wt% and 20.1 wt% were obtained for PFDCHQ from TGA results in nitrogen and air atmosphere, respectively. The thermal degradation mechanism of PFDCHQ was investigated by TG-FTIR and Py-GC/MS. The limiting oxygen index (LOI) of EP-5 with 5 wt% loading of PFDCHQ increased to 32.0% and the UL-94 V-0 rating was achieved, showing a notable blowing-out effect. In contrast to EP-0, the peak of the heat release rate (pHRR) and total heat release (THR) of EP-5 decreased by 18.0% and 10.3%. The flame retardant mechanism of PFDCHQ in epoxy resin was studied by TG-FTIR, SEM and Raman. SEM and Raman results indicated the formation of coherent and dense char residue with high degree of graphitization due to the incorporation of PFDCHQ. In UL-94, the blowing-out effect dominantly accounted for the enhanced flame retardancy in combination with optimized char structure. Furthermore, the addition of PFDCHQ improved the Young's modulus compared to EP-0