Understanding a Dynamic World: Dynamic Motion Estimation for Autonomous Driving Using LIDAR

In a society that is heavily reliant on personal transportation, autonomous vehicles present an increasingly intriguing technology. They have the potential to save lives, promote efficiency, and enable mobility. However, before this vision becomes a reality, there are a number of challenges that must be solved. One key challenge involves problems in dynamic motion estimation, as it is critical for an autonomous vehicle to have an understanding of the dynamics in its environment for it to operate safely on the road. Accordingly, this thesis presents several algorithms for dynamic motion estimation for autonomous vehicles. We focus on methods using light detection and ranging (LIDAR), a prevalent sensing modality used by autonomous vehicle platforms, due to its advantages over other sensors, such as cameras, including lighting invariance and fidelity of 3D geometric data. First, we propose a dynamic object tracking algorithm. The proposed method takes as input a stream of LIDAR data from a moving object collected by a multi-sensor platform. It generates an estimate of its trajectory over time and a point cloud model of its shape. We formulate the problem similarly to simultaneous localization and mapping (SLAM), allowing us to leverage existing techniques. Unlike prior work, we properly handle a stream of sensor measurements observed over time by deriving our algorithm using a continuous-time estimation framework. We evaluate our proposed method on a real-world dataset that we collect. Second, we present a method for scene flow estimation from a stream of LIDAR data. Inspired by optical flow and scene flow from the computer vision community, our framework can estimate dynamic motion in the scene without relying on segmentation and data association while still rivaling the results of state-of-the-art object tracking methods. We design our algorithms to exploit a graphics processing unit (GPU), enabling real-time performance. Third, we leverage deep learning tools to build a feature learning framework that allows us to train an encoding network to estimate features from a LIDAR occupancy grid. The learned feature space describes the geometric and semantic structure of any location observed by the LIDAR data. We formulate the training process so that distances in this learned feature space are meaningful in comparing the similarity of different locations. Accordingly, we demonstrate that using this feature space improves our estimate of the dynamic motion in the environment over time. In summary, this thesis presents three methods to aid in understanding a dynamic world for autonomous vehicle applications with LIDAR. These methods include a novel object tracking algorithm, a real-time scene flow estimation method, and a feature learning framework to aid in dynamic motion estimation. Furthermore, we demonstrate the performance of all our proposed methods on a collection of real-world datasets.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147587/1/aushani_1.pd

Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don\u27t have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don\u27t yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet

Application of mixed and virtual reality in geoscience and engineering geology

Visual learning and efficient communication in mining and geotechnical practices is crucial, yet often challenging. With the advancement of Virtual Reality (VR) and Mixed Reality (MR) a new era of geovisualization has emerged. This thesis demonstrates the capabilities of a virtual continuum approach using varying scales of geoscience applications. An application that aids analyses of small-scale geological investigation was constructed using a 3D holographic drill core model. A virtual core logger was also developed to assist logging in the field and subsequent communication by visualizing the core in a complementary holographic environment. Enriched logging practices enhance interpretation with potential economic and safety benefits to mining and geotechnical infrastructure projects. A mine-scale model of the LKAB mine in Sweden was developed to improve communication on mining induced subsidence between geologists, engineers and the public. GPS, InSAR and micro-seismicity data were hosted in a single database, which was geovisualized through Virtual and Mixed Reality. The wide array of applications presented in this thesis illustrate the potential of Mixed and Virtual Reality and improvements gained on current conventional geological and geotechnical data collection, interpretation and communication at all scales from the micro- (e.g. thin section) to the macro- scale (e.g. mine)

Gas in engine cooling systems: occurrence, effects and mitigation

The presence of gas in engine liquid cooling systems can have severe consequences for engine efficiency and life. The presence of stagnant, trapped gases will result in cooling system hotspots, causing gallery wall degradation through thermal stresses, fatigue and eventual cracking. The presence of entrained, transient gases in the coolant flow will act to reduce its bulk thermal properties and the performance of the system s coolant pump; critically the liquid flow rate, which will severely affect heat transfer throughout the engine and its ancillaries. The hold-up of gas in the pump s impeller may cause the dynamic seal to run dry, without lubrication or cooling. This poses both an immediate failure threat should the seal overheat and rubber components melt and a long term failure threat from intermittent quench cooling, which causes deposit formation on sealing faces acting to abrade and reduce seal quality. Bubbles in the coolant flow will also act as nucleation sites for cavitation growth. This will reduce the Net Positive Suction Head available (NPSHA) in the coolant flow, exacerbating cavitation and its damaging effects in locations such as the cylinder cooling liners and the pump s impeller. This thesis has analysed the occurrence of trapped gas (air) during the coolant filling process, its behaviour and break-up at engine start, the two-phase character of the coolant flow these processes generate and the effects it has on coolant pump performance. Optical and parametric data has been acquired in each of these studies, providing an understanding of the physical processes occurring, key variables and a means of validating numerical (CFD) code of integral processes. From the fundamental understanding each study has provided design rules, guidelines and validated tools have been developed, helping cooling system designers minimise the occurrence of trapped air during coolant filling, promote its breakup at engine start and to minimise its negative effects in the centrifugal coolant pump. It was concluded that whilst ideally the prevention of cooling system gases should be achieved at source, they are often unavoidable. This is due to the cost implications of finding a cylinder head gasket capable of completely sealing in-cylinder combustion pressures, the regular use of nucleate boiling regimes for engine cooling and the need to design cooling channel geometries to cool engine components and not necessarily to avoid fill entrapped air. Using the provided rules and models, it may be ensured stagnant air is minimised at source and avoided whilst an engine is running. However, to abate the effects of entrained gases in the coolant pump through redesign is undesirable due to the negative effects such changes have on a pump s efficiency and cavitation characteristics. It was concluded that the best solution to entrained gases, unavoidable at source, is to remove them from the coolant flow entirely using phase separation device(s)

The design and development of multi-agent based RFID middleware system for data and devices management

Thesis (D. Tech. (Electrical Engineering)) - Central University of technology, Free State, 2012Radio frequency identification technology (RFID) has emerged as a key technology for automatic identification and promises to revolutionize business processes. While RFID technology adoption is improving rapidly, reliable and widespread deployment of this technology still faces many significant challenges. The key deployment challenges include how to use the simple, unreliable raw data generated by RFID deployments to make business decisions; and how to manage a large number of deployed RFID devices. In this thesis, a multi-agent based RFID middleware which addresses some of the RFID data and device management challenges was developed. The middleware developed abstracts the auto-identification applications from physical RFID device specific details and provides necessary services such as device management, data cleaning, event generation, query capabilities and event persistence. The use of software agent technology offers a more scalable and distributed system architecture for the proposed middleware. As part of a multi-agent system, application-independent domain ontology for RFID devices was developed. This ontology can be used or extended in any application interested with RFID domain ontology. In order to address the event processing tasks within the proposed middleware system, a temporal-based RFID data model which considers both applications’ temporal and spatial granules in the data model itself for efficient event processing was developed. The developed data model extends the conventional Entity-Relationship constructs by adding a time attribute to the model. By maintaining the history of events and state changes, the data model captures the fundamental RFID application logic within the data model. Hence, this new data model supports efficient generation of application level events, updating, querying and analysis of both recent and historical events. As part of the RFID middleware, an adaptive sliding-window based data cleaning scheme for reducing missed readings from RFID data streams (called WSTD) was also developed. The WSTD scheme models the unreliability of the RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. The WSTD scheme is capable of efficiently coping with both environmental variations and tag dynamics by automatically and continuously adapting its cleaning window size, based on observed readings

Trade-off analysis of modes of data handling for earth resources (ERS), volume 1

Data handling requirements are reviewed for earth observation missions along with likely technology advances. Parametric techniques for synthesizing potential systems are developed. Major tasks include: (1) review of the sensors under development and extensions of or improvements in these sensors; (2) development of mission models for missions spanning land, ocean, and atmosphere observations; (3) summary of data handling requirements including the frequency of coverage, timeliness of dissemination, and geographic relationships between points of collection and points of dissemination; (4) review of data routing to establish ways of getting data from the collection point to the user; (5) on-board data processing; (6) communications link; and (7) ground data processing. A detailed synthesis of three specific missions is included

Regional-scale controls on rockfall occurrence

Rockfalls exert a first-order control on the rate of rock wall retreat on mountain slopes and on coastal rock cliffs. Their occurrence is conditioned by a combination of intrinsic (resisting) and extrinsic (driving) processes, yet determining the exact effects of these processes on rockfall activity and the resulting cliff erosion remains difficult. Although rockfall activity has been monitored extensively in a variety of settings, high-resolution observations of rockfall occurrence on a regional scale are scarce. This is partly owing to difficulties in adequately quantifying the full range of possible rockfall volumes with sufficient accuracy and completeness, and at a scale that exceeds the influence of localised controls on rockfalls. This lack of insight restricts our ability to abstract patterns, to identify long-term changes in behaviour, and to assess how rock slopes respond to changes in both structural and environmental conditions, without resorting to a space for-time substitution. This thesis develops a workflow, from novel data collection to analysis, which is tailored to monitoring rockfall activity and the resulting cliff retreat continuously (in space), in 3D, and over large spatial scales $(> 10^4 m)$. The approach is tested by analysing rockfall activity and the resulting erosion recorded along 20.5 km of near-vertical coastal cliffs, in what is considered as the first multi-temporal detection of rockfalls at a regional-scale and in full 3D. The resulting data are then used to derive a quantitative appraisal of along-coast variations in the geometric properties of exposed discontinuity surfaces, to assess the extent to which these drive patterns in the size and shape of the rockfalls observed. High-resolution field monitoring is then undertaken along a subsection of the coastline $(> 10^2 m)$, where cliff lithology and structure are approximately uniform, in order to quantify spatial variations in wave loading characteristics and to relate these to local morphological conditions, which can act as a proxy for wave loading characteristics. The resulting rockfall inventory is analysed to identify the characteristics of rock slope change that only become apparent when assessed at this scale, placing bounds on data previously collected more locally $(< 10^2 m)$. The data show that spatial consistencies in the distribution of rockfall shape and volume through time approximately follow the geological setting of the coastline, but that variations in the strength of these consistencies are likely to be conditioned by differences in local processes and morphological controls between sites. These results are used to examine the relationships between key metrics of erosion, structural, and morphological controls, which ultimately permits the identification of areas where patterns of erosion are dominated by either intrinsic or extrinsic processes, or a mixture of both. Uniquely, the methodologies and data presented here mark a step-change in our ability to understand the competing effects of different processes in determining the magnitude and frequency of rockfall activity, and the resulting cliff erosion. The findings of this research hold considerable implications for our understanding of rockfalls, and for monitoring, modelling, and managing actively failing rock slopes