Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments

Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

Low Code Development Platform Adoption: A Research Model

Although Low Code Development Platforms (LCDP) promise efficiency and effectiveness improvements for organisations when adopted, research on LCDP adoption lacks a theoretical foundation. This research-in-progress paper proposes a research model to explain LCDP adoption. The research model combines two theoretical lenses, including social and technical factors referring to the socio-technical systems theory, complementing the environmental factors captured in the Technology – Environment – Organisation model. As single factors may not be sufficient to explain LCDP adoption, this paper introduces combinations of factors that balance social, technical, and environmental factors. In this stage, the paper’s contribution to research is a first theoretically grounded but tentative model to explain LCDP adoption. The expected results of this study provide combinations of factors to indicate one or more paths for LCDP adoption

Adolescent mental health problems in early stages of the COVID-19 pandemic were masked by lockdown measures and restrictions.

In the BJPsych Open Wong et al examined the influence of lockdown stringency during early stages of the COVID-19 pandemic on psychiatric emergency presentations among children and adolescents from ten countries. Data from March and April 2019 were compared with the same time frame in 2020, with particular focus on self-harm admissions. In this editorial, the publication is summarised and potential implications for the field and future studies are discussed

Learning Observation Models with Incremental Non-Differentiable Graph Optimizers in the Loop for Robotics State Estimation

We consider the problem of learning observation models for robot state estimation with incremental non-differentiable optimizers in the loop. Convergence to the correct belief over the robot state is heavily dependent on a proper tuning of observation models which serve as input to the optimizer. We propose a gradient-based learning method which converges much quicker to model estimates that lead to solutions of much better quality compared to an existing state-of-the-art method as measured by the tracking accuracy over unseen robot test trajectories.Comment: 6 pages, 4 figures. Published at the Differentiable Almost Everything Workshop of the 40th International Conference on Machine Learnin