Localisation of ground range sensors using overhead imagery

This thesis is about outdoor localisation using range sensors as an active sensor and cheap, publicly available satellite or `overhead' imagery as a prior map. Range sensors such as lidars and spinning FMCW radars are ideal for large-scale, outdoor autonomous navigation due to their long sensing range, invariance to lighting conditions, and robustness against weather changes. Nevertheless, existing methods for range sensor localisation typically rely on prior maps collected from a previous mapping phase. On the other hand, off-the-shelf overhead imagery, such as public satellite images, is readily available almost anywhere in the world and can be acquired easily from the internet with little cost or effort. Public overhead imagery can capture geometric cues of the scene also observable by ground lidars and radars, therefore having the capability to act as a map for range sensor localisation. In particular, in corner case scenarios where the prior sensory map is unusable or unavailable, for example if the robot travels to a place it has not visited before, public overhead imagery can act as an alternative map source for range sensor localisation as a fall-back choice. Under normal operation conditions, the localisation result by comparing range sensor data against overhead imagery can act as an additional information source for redundancy. In this thesis, we present various methods to solve the localisation of a ground range sensor using overhead imagery by learning from data, enabling them to adapt to different environments. This surpasses the methods in literature which employ hand-crafted features designed for only specific types of scenery. Specifically, we address both topological localisation, also known as place recognition, and metric localisation in overhead imagery maps. Furthermore, we investigate self-supervised strategies that allow the tasks to be learned without accurate ground truth data

Laboratory characterization of directional dependence of permeability for porous asphalt mixtures

Water permeability is an important property for porous asphalt mixtures. Previous numerical modeling showed that the permeability of the porous asphalt mixtures varies in different directions and a single permeability cannot accurately evaluate the mixture's directional permeability. To investigate the direction-dependent water permeability of the porous asphalt mixtures, a unidirectional permeameter was used to measure the permeability in twelve directions in the vertical plane (parallel to compaction direction) and twelve directions in the horizontal plane (perpendicular to the compaction direction) on two open graded friction course (OGFC) mixtures with different nominal maximum aggregate sizes, i.e., OGFC-13 and OGFC-10. Furthermore, a new multidirectional permeameter was designed which can control the rainfall intensity and adjust transverse slope to simulate the actual water flow process in pavement. The multidirectional permeability and void saturation of eight porous asphalt mixtures were determined by the multidirectional permeameter. Results show that the porous asphalt mixtures demonstrate direction-dependent permeability properties in both vertical and horizontal planes, whereas the dependence is less in the horizontal plane than that in the vertical plane. In the vertical plane, the minimum permeability occurs in the vertical direction and the maximum value occurs in the horizontal direction. In the horizontal plane, the permeability differs in different directions, but has no obvious relationship with directions. Increasing the air void content and the nominal maximum aggregate size of the mixtures can reduce the directional difference of the permeability. The void inside porous mixture cannot be entirely occupied by water when surface runoff occurs. Increasing the air void content and aggregate particle size can lead to an increase of the permeability and the void saturation in the porous asphalt mixtures

Reasoning about the impacts of information sharing

Shared information can benefit an agent, allowing others to aid it in its goals. However, such information can also harm, for example when malicious agents are aware of these goals, and can then thereby subvert the goal-maker's plans. In this paper we describe a decision process framework allowing an agent to decide what information it should reveal to its neighbours within a communication graph in order to maximise its utility. We assume that these neighbours can pass information onto others within the graph. The inferences made by agents receiving the messages can have a positive or negative impact on the information providing agent, and our decision process seeks to assess how a message should be modified in order to be most beneficial to the information producer. Our decision process is based on the provider's subjective beliefs about others in the system, and therefore makes extensive use of the notion of trust with regards to the likelihood that a message will be passed on by the receiver, and the likelihood that an agent will use the information against the provider. Our core contributions are therefore the construction of a model of information propagation; the description of the agent's decision procedure; and an analysis of some of its properties

Heterogeneity in Pkp2-Containing Junctions in the Adult Epicardium

Honors (Bachelor's)Cell and Molecular BiologyUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/98851/1/syqtang.pd

Point-based metric and topological localisation between lidar and overhead imagery

In this paper, we present a method for solving the localisation of a ground lidar using overhead imagery only. Public overhead imagery such as Google satellite images are readily available resources. They can be used as the map proxy for robot localisation, relaxing the requirement for a prior traversal for mapping as in traditional approaches. While prior approaches have focused on the metric localisation between range sensors and overhead imagery, our method is the first to learn both place recognition and metric localisation of a ground lidar using overhead imagery, and also outperforms prior methods on metric localisation with large initial pose offsets. To bridge the drastic domain gap between lidar data and overhead imagery, our method learns to transform an overhead image into a collection of 2D points, emulating the resulting point-cloud scanned by a lidar sensor situated near the centre of the overhead image. After both modalities are expressed as point sets, point-based machine learning methods for localisation are applied

Reasoning about Uncertain Information and Conflict Resolution through Trust Revision

Publisher PD

Impact of biological clogging on the barrier performance of landfill liners

The durability of landfill mainly relies on the anti-seepage characteristic of liner system. The accumulation of microbial biomass is effective in reducing the hydraulic conductivity of soils. This study aimed at evaluating the impact of the microorganism on the barrier performance of landfill liners. According to the results, Escherichia coli. produced huge amounts of extracellular polymeric substances and coalesced to form a confluent plugging biofilm. This microorganism eventually resulted in the decrease of soil permeability by 81%–95%. Meanwhile, the increase of surface roughness inside the internal pores improved the adhesion between microorganism colonization and particle surface. Subsequently, an extensive parametric sensitivity analysis was undertaken for evaluating the contaminant transport in landfill liners. Decreasing the hydraulic conductivity from 1 × 10−8 m/s to 1 × 10−10 m/s resulted in the increase of the breakthrough time by 345.2%. This indicates that a low hydraulic conductivity was essential for the liner systems to achieve desirable barrier performance