LiDAR Enhanced Structure-from-Motion

Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.Comment: 6 pages plus reference. Work has been submitted to ICRA 202

Targetless Extrinsic Calibration of Stereo Cameras, Thermal Cameras, and Laser Sensors in the Wild

The fusion of multi-modal sensors has become increasingly popular in autonomous driving and intelligent robots since it can provide richer information than any single sensor, enhance reliability in complex environments. Multi-sensor extrinsic calibration is one of the key factors of sensor fusion. However, such calibration is difficult due to the variety of sensor modalities and the requirement of calibration targets and human labor. In this paper, we demonstrate a new targetless cross-modal calibration framework by focusing on the extrinsic transformations among stereo cameras, thermal cameras, and laser sensors. Specifically, the calibration between stereo and laser is conducted in 3D space by minimizing the registration error, while the thermal extrinsic to the other two sensors is estimated by optimizing the alignment of the edge features. Our method requires no dedicated targets and performs the multi-sensor calibration in a single shot without human interaction. Experimental results show that the calibration framework is accurate and applicable in general scenes.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

VoxDet: Voxel Learning for Novel Instance Detection

Detecting unseen instances based on multi-view templates is a challenging problem due to its open-world nature. Traditional methodologies, which primarily rely on 2D representations and matching techniques, are often inadequate in handling pose variations and occlusions. To solve this, we introduce VoxDet, a pioneer 3D geometry-aware framework that fully utilizes the strong 3D voxel representation and reliable voxel matching mechanism. VoxDet first ingeniously proposes template voxel aggregation (TVA) module, effectively transforming multi-view 2D images into 3D voxel features. By leveraging associated camera poses, these features are aggregated into a compact 3D template voxel. In novel instance detection, this voxel representation demonstrates heightened resilience to occlusion and pose variations. We also discover that a 3D reconstruction objective helps to pre-train the 2D-3D mapping in TVA. Second, to quickly align with the template voxel, VoxDet incorporates a Query Voxel Matching (QVM) module. The 2D queries are first converted into their voxel representation with the learned 2D-3D mapping. We find that since the 3D voxel representations encode the geometry, we can first estimate the relative rotation and then compare the aligned voxels, leading to improved accuracy and efficiency. Exhaustive experiments are conducted on the demanding LineMod-Occlusion, YCB-video, and the newly built RoboTools benchmarks, where VoxDet outperforms various 2D baselines remarkably with 20% higher recall and faster speed. To the best of our knowledge, VoxDet is the first to incorporate implicit 3D knowledge for 2D tasks.Comment: 17 pages, 10 figure

Extremum Seeking Control of Hybrid Ground Source Heat Pump System

The ground source heat pump (GSHP) technology is a renewable alternative for space conditioning by rejecting/absorbing heat to/from the ground, which has demonstrated higher energy efficiency for residential and commercial buildings. As the system capacity is limited by the initial cost of construction of ground-loop heat exchanger (GHE), developing the so-called Hybrid GSHP system by utilizing supplemental heat rejecters such as cooling towers has emerged as a cost-effective alternative. In practice, operational efficiency of Hybrid GSHP system mainly depends on 1) the actual characteristics of heat pump, cooling tower, GHE and other equipment; 2) ambient air and ground conditions. In particular, the GHE heat transfer is heavily affected by the ground thermal characteristics which, however, is difficult and expensive in practice to determine due to the complexity of soil type and distribution. In addition, the actual cooling tower characteristics can vary significantly. Such uncertainties bring forth dramatic difficulty for successful application of model based control or optimization methods. In this study, an extremum seeking control (ESC) strategy is proposed for efficient operation of a hybrid GSHP system with cooling tower, which minimizes the total power (i.e. GHE loop water pump, cooling tower fan and pump, and the heat-pump compressor) consumption by tuning the air-flow rate of the cooling tower fan and the GHE loop water flow rate. To evaluate the proposed control method, a Modelica based model of the Hybrid GSHP system is developed by utilizing the Buildings Library developed by the Lawrence Berkeley National Laboratory, which consists of a 20-borehole GHE, a water-to-water heat pump, a counter-flow cooling tower and a plate heat exchanger. The transient conduction model of vertical GHE in the Buildings Library is adopted, which is based on a finite-volume method inside the borehole and cylindrical source model outside the borehole. A variable-flow water pump model is constructed for the GHE water loop, which gives power consumption under different operating scenarios. A cooling tower model in the Buildings Library is adopted, which is a static polynomial model based on a York cooling tower correlation. The relative air flow rate can be regulated to maintain the leaving water temperature at the setpoint, and then the corresponding fan power consumption is obtained. The heat pump model is based on the evaporator temperature, condenser temperature and Carnot efficiency. An inner-loop proportional-integral (PI) controller is implemented to regulate the evaporator leaving water temperature at 7 deg-C. Under the air wet-bulb temperature of 35 deg-C and dry-bulb temperature 23 deg-C, steady-state simulation of the plant model yields the static map of the total power with respect to the cooling tower relative air flow rate and the GHE water flow rate, which indicates about 25% power variation across the adjustable range of inputs. Simulation was conducted in two conditions: change in evaporator inlet water temperature and change in ambient air condition. The simulation study under way is to validate the effectiveness of the proposed ESC strategy, and the potential for energy saving will also be evaluated

YOLOv5s-gnConv: detecting personal protective equipment for workers at height

IntroductionFalls from height (FFH) accidents can devastate families and individuals. Currently, the best way to prevent falls from heights is to wear personal protective equipment (PPE). However, traditional manual checking methods for safety hazards are inefficient and difficult to detect and eliminate potential risks.MethodsTo better detect whether a person working at height is wearing PPE or not, this paper first applies field research and Python crawling techniques to create a dataset of people working at height, extends the dataset to 10,000 images through data enhancement (brightness, rotation, blurring, and Moica), and categorizes the dataset into a training set, a validation set, and a test set according to the ratio of 7:2:1. In this study, three improved YOLOv5s models are proposed for detecting PPE in construction sites with many open-air operations, complex construction scenarios, and frequent personnel changes. Among them, YOLOv5s-gnconv is wholly based on the convolutional structure, which achieves effective modeling of higher-order spatial interactions through gated convolution (gnConv) and cyclic design, improves the performance of the algorithm, and increases the expressiveness of the model while reducing the network parameters.ResultsExperimental results show that YOLOv5s-gnconv outperforms the official model YOLOv5s by 5.01%, 4.72%, and 4.26% in precision, recall, and mAP_0.5, respectively. It better ensures the safety of workers working at height.DiscussionTo deploy the YOLOv5s-gnConv model in a construction site environment and to effectively monitor and manage the safety of workers at height, we also discuss the impacts and potential limitations of lighting conditions, camera angles, and worker movement patterns

Improving Barrier Properties of PET by Depositing a Layer of DLC Films on Surface

The diamond-like carbon films (DLC films) depositing on the Poly (ethylene terephthalate) (PET) surface are obtained by plasma-enhanced chemical vapor deposition (PECVD), and the working gases are acetylene and argon gas. Surface morphology and the internal structure of DLC films are investigated by using Raman and FESEM, and the barrier properties of PET films which have been deposited the DLC films are tested in this paper. The results show that the deposition process parameters have an important effect on structure and performance of DLC films. It is shown that the diamond-like carbon films prepared by PECVD system are an amorphous carbon films which mixed with sp3 bond and sp2 bond. The best oxygen barrier property and water vapor barrier property of PET films are increased by 11 times and 12 times, respectively, in which the ID/IG ratio of the DLC film is nearly 0.76, and the sp3 content is about 40%

FoundLoc: Vision-based Onboard Aerial Localization in the Wild

Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms