Efficient and elastic LiDAR reconstruction for large-scale exploration tasks

High-quality reconstructions and understanding the environment are essential for robotic tasks such as localisation, navigation and exploration. Applications like planners and controllers can make decisions based on them. International competitions such as the DARPA Subterranean Challenge demonstrate the difficulties that reconstruction methods must address in the real world, e.g. complex surfaces in unstructured environments, accumulation of localisation errors in long-term explorations, and the necessity for methods to be scalable and efficient in large-scale scenarios. Guided by these motivations, this thesis presents a multi-resolution volumetric reconstruction system, supereight-Atlas (SE-Atlas). SE-Atlas efficiently integrates long-range LiDAR scans with high resolution, incorporates motion undistortion, and employs an Atlas of submaps to produce an elastic 3D reconstruction. These features address limitations of conventional reconstruction techniques that were revealed in real-world experiments of an initial active perceptual planning prototype. Our experiments with SE-Atlas show that it can integrate LiDAR scans at 60m range with ∼5 cm resolution at ∼3 Hz, outperforming state-of-the-art methods in integration speed and memory efficiency. Reconstruction accuracy evaluation also proves that SE-Atlas can correct the map upon SLAM loop closure corrections, maintaining global consistency. We further propose four principled strategies for spawning and fusing submaps. Based on spatial analysis, SE-Atlas spawns new submaps when the robot transitions into an isolated space, and fuses submaps of the same space together. We focused on developing a system which scales against environment size instead of exploration length. A new formulation is proposed to compute relative uncertainties between poses in a SLAM pose graph, improving submap fusion reliability. Our experiments show that the average error in a large-scale map is approximately 5 cm. A further contribution was incorporating semantic information into SE-Atlas. A recursive Bayesian filter is used to maintain consistency in per-voxel semantic labels. Semantics is leveraged to detect indoor-outdoor transitions and adjust reconstruction parameters online

The Importance of Coordinate Frames in Dynamic SLAM

Most Simultaneous localisation and mapping (SLAM) systems have traditionally assumed a static world, which does not align with real-world scenarios. To enable robots to safely navigate and plan in dynamic environments, it is essential to employ representations capable of handling moving objects. Dynamic SLAM is an emerging field in SLAM research as it improves the overall system accuracy while providing additional estimation of object motions. State-of-the-art literature informs two main formulations for Dynamic SLAM, representing dynamic object points in either the world or object coordinate frame. While expressing object points in a local reference frame may seem intuitive, it may not necessarily lead to the most accurate and robust solutions. This paper conducts and presents a thorough analysis of various Dynamic SLAM formulations, identifying the best approach to address the problem. To this end, we introduce a front-end agnostic framework using GTSAM that can be used to evaluate various Dynamic SLAM formulations.Comment: 7 pages, 4 figures, submitted to ICRA 202

Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot

In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure

Superlens-Assisted Laser Nanostructuring of Long Period Optical Fiber Gratings (LPGs) for Enhanced Refractive Index Sensing

We present an innovative method to enhance Long Period Optical Fiber Gratings (LPGs) for refractive index sensing using microsphere-assisted superlens laser nanostructuring. This technique involves self-assembling a silica microsphere monolayer on LPGs' outer surface, followed by pulsed laser irradiation to generate nanoholes (300-500 nm) forming nanohole-structured LPGs (NS-LPGs). In experiments, two nanohole densities were compared for their impact on sensing performance in sucrose and glycerin solutions. The nanostructured NS-LPGs showed improved sensitivity by 16.08% and 19.57% compared to regular LPGs, with higher nanohole density yielding greater enhancement. Importantly, the permanent nanohole structures ensure durability in harsh environments, surpassing conventional surface-coating-based LPGs. Further improvements can be achieved by refining nanostructuring density and controlling nanohole size and depth. Our work represents a notable advancement in LPG sensor engineering, prioritizing surface nanostructuring over nano-coating, promising enhanced refractive index sensing applications.Comment: 13 pages, 5 figure

Enhancing Security with Superlens-Enabled Laser Direct Marking of Anti-counterfeiting DotCode

We report a novel anti-counterfeiting laser marking technology based on superlens- assisted nanoscale marking of 2D Dotcodes, which replaces conventional TEXT or other 2D code schemes for enhanced security

Generation Expansion Planning with Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets\u27 planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning

Time To Rethink Engineering Outreach?

Starting with the research question ‘Does engineering outreach work?’ this paper looks at the often ‘sticky’ subject of the validity of engineering outreach in UK High Schools. It examines how Engineering Outreach Activities are conceptualised by external bodies (RAEng., 2016) and critiques the complex range of practical experiential engineering educational interventions offered in school (Neon, 2023, STEM learning, 2023). Drawing upon the findings of, what is, a small single strand of a much larger multi-method, longitudinal analysis of Engineering Education Outreach Activities provided across the West Midlands region of the UK (LBEEP, 2023) ], the paper provides a unique insight and descriptive analysis of engineering outreach in schools. The findings section comprises a comparative analysis of the socio-economic background of schools before looking at the gender breakdown of outreach participants. The various engineering interventions provided are briefly discussed before consideration is given as to how sustainable current engineering outreach activities are. Finally, in questioning whether the UK’s current approach of providing engineering education experiences in the form of what are often idiosyncratic, short term episodic activities, the paper questions the financial, pedagogic and practical wisdom of confining engineering education to ‘outreach’. The conclusion suggests that it’s time for a sea-change in how we, as a society, teach children and young people about engineering and suggests that perhaps it is time to embed the subject into more established areas of study such as maths and science but also in history and social science

SuperNANO: Enabling Nanoscale Laser Anti-Counterfeiting Marking and Precision Cutting with Super-Resolution Imaging

In this paper, we present a unique multi-functional super-resolution instrument, the Super- NANO system, which integrates real-time super-resolution imaging with direct laser nanofabrication capabilities. Central to the functionality of the SuperNANO system is its capacity for simultaneous nanoimaging and nanopatterning, enabling the creation of anti-counterfeiting markings and precision cutting with exceptional accuracy. The SuperNANO system, featuring a unibody superlens objective, achieves a resolution ranging from 50 to 320 nm. We showcase the instrument’s versatility through its application in generating high-security anti-counterfeiting features on an aluminum film. These ‘invisible’ security features, which are nanoscale in dimension, can be crafted with arbitrary shapes at designated locations. Moreover, the system’s precision is further evidenced by its ability to cut silver nanowires to a minimum width of 50 nm. The integrated imaging and fabricating functions of the SuperNANO make it a pivotal tool for a variety of applications, including nanotrapping, sensing, cutting, welding, drilling, signal enhancement, detection, and nanoscale laser treatment.<br/

PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations

Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results. Generally, the focus has been on historical-value-based models, which rely on past observations to predict future series. Notably, a new trend has emerged with time-index-based models, offering a more nuanced understanding of the continuous dynamics underlying time series. Unlike these two types of models that aggregate the information of spatial domains or temporal domains, in this paper, we consider multivariate time series as spatiotemporal data regularly sampled from a continuous dynamical system, which can be represented by partial differential equations (PDEs), with the spatial domain being fixed. Building on this perspective, we present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers, following the encoding-integration-decoding operations. Our extensive experimentation across seven diverse real-world LMTF datasets reveals that PDETime not only adapts effectively to the intrinsic spatiotemporal nature of the data but also sets new benchmarks, achieving state-of-the-art result