Safety mechanisms for the reliable operation of 3D vehicles

The safety and reliability of unmanned vehicles is a growing concern in our modern society. This work proposes and implements mechanisms to minimize risks in the operation of 3D vehicles. A brief analysis is performed to identify high priority risks and low complexity solutions are proposed in order to avoid or minimize their impact. To cope with critical power failures, an autonomous current monitoring system was studied and implemented after analyzing two different techniques: resistive and magnetic current sensing. Furthermore, a fall detection system capable of detecting rotational and free falls was developed and evaluated. Lastly, an obstacle detection and avoidance system relying on multiple smart sensors was proposed. Several simulation tests were performed for different velocities to obtain processing delays and stopping times and thus, the minimal safe flying distance for the avoidance of obstacles.A segurança na operação fiável de veículos não tripulados é uma preocupação crescente na nossa sociedade moderna. Este trabalho propõe e implementa mecanismos para minimizar os riscos no manuseamento destes veículos. Uma breve análise é realizada para identificar os componentes com maior risco de ocorrerem problemas e soluções de baixa complexidade são propostas a fim de evitar ou minimizar o seu impacto. Para lidar com falhas de energia críticas, um sistema de monitorização de corrente foi estudado e implementado após analisar duas técnicas diferentes: detecção de corrente resistiva e magnética. Além disso, foi desenvolvido e avaliado um sistema de detecção de quedas rotacionais e livres. Por último, foi proposto um sistema de detecção e anti-colisão de obstáculos baseado em múltiplos sensores inteligentes. Diversos testes de simulação foram realizados para obter atrasos de processamento e tempos de travagem. Deste modo foi possível calcular a distância de segurança mínima de travagem face à detecção de um obstáculo

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Improving Image Classification with Location Context

With the widespread availability of cellphones and cameras that have GPS capabilities, it is common for images being uploaded to the Internet today to have GPS coordinates associated with them. In addition to research that tries to predict GPS coordinates from visual features, this also opens up the door to problems that are conditioned on the availability of GPS coordinates. In this work, we tackle the problem of performing image classification with location context, in which we are given the GPS coordinates for images in both the train and test phases. We explore different ways of encoding and extracting features from the GPS coordinates, and show how to naturally incorporate these features into a Convolutional Neural Network (CNN), the current state-of-the-art for most image classification and recognition problems. We also show how it is possible to simultaneously learn the optimal pooling radii for a subset of our features within the CNN framework. To evaluate our model and to help promote research in this area, we identify a set of location-sensitive concepts and annotate a subset of the Yahoo Flickr Creative Commons 100M dataset that has GPS coordinates with these concepts, which we make publicly available. By leveraging location context, we are able to achieve almost a 7% gain in mean average precision

Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors

We present a method to infer 3D pose and shape of vehicles from a single image. To tackle this ill-posed problem, we optimize two-scale projection consistency between the generated 3D hypotheses and their 2D pseudo-measurements. Specifically, we use a morphable wireframe model to generate a fine-scaled representation of vehicle shape and pose. To reduce its sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse representation which improves robustness. We also integrate three task priors, including unsupervised monocular depth, a ground plane constraint as well as vehicle shape priors, with forward projection errors into an overall energy function.Comment: Proc. of the AAAI, September 201