Robust Localization in 3D Prior Maps for Autonomous Driving.

In order to navigate autonomously, many self-driving vehicles require precise localization within an a priori known map that is annotated with exact lane locations, traffic signs, and additional metadata that govern the rules of the road. This approach transforms the extremely difficult and unpredictable task of online perception into a more structured localization problem—where exact localization in these maps provides the autonomous agent a wealth of knowledge for safe navigation. This thesis presents several novel localization algorithms that leverage a high-fidelity three-dimensional (3D) prior map that together provide a robust and reliable framework for vehicle localization. First, we present a generic probabilistic method for localizing an autonomous vehicle equipped with a 3D light detection and ranging (LIDAR) scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the z-height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Second, we propose a visual localization strategy that replaces the expensive 3D LIDAR scanners with significantly cheaper, commodity cameras. In doing so, we exploit a graphics processing unit to generate synthetic views of our belief environment, resulting in a localization solution that achieves a similar order of magnitude error rate with a sensor that is several orders of magnitude cheaper. Finally, we propose a visual obstacle detection algorithm that leverages knowledge of our high-fidelity prior maps in its obstacle prediction model. This not only provides obstacle awareness at high rates for vehicle navigation, but also improves our visual localization quality as we are cognizant of static and non-static regions of the environment. All of these proposed algorithms are demonstrated to be real-time solutions for our self-driving car.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133410/1/rwolcott_1.pd

Walking the walk: a phenomenological study of long distance walking

Evidence suggests that regular walking can elicit significant psychological benefits although little evidence exists concerning long distance walking. The purpose of this study was to provide detailed accounts of the experiences of long distance walkers. Phenomenological interviews were conducted with six long distance walkers. Data were transcribed verbatim before researchers independently analyzed the transcripts. Participants reported a cumulative effect with positive feelings increasing throughout the duration of the walk. Long distance walking elicited positive emotions, reduced the effects of life-stress, and promoted an increased sense of well-being and personal growth. Results are aligned to theories and concepts from positive psychology

Biological and Environmental Influences on Parturition Date and Birth Mass of a Seasonal Breeder

<div><p>Natal features (e.g. Julian birth date and birth mass) often have fitness consequences and can be influenced by endogenous responses by the mother to seasonal fluctuations in nutritional quality and photoperiodic cues. We sought to further understand the biological and environmental factors that influence the natal features of a polytocous species in an environment with constant nutritional resources and limited seasonal variation. During a 36-year study we assessed the influence of biological factors (maternal age and litter type [i.e., litter size and sexual composition]) and environmental factors (total precipitation and mean maximum temperature during months encompassing conception, the last trimester of gestation, and the entire length of gestation) on Julian birth date and birth mass using linear-mixed effects models. Linear and quadratic functions of maternal age influenced both natal features with earliest Julian birth dates and heaviest birth masses occurring at prime-age and older individuals, which ranged from 5–9 years of age. Litter type influenced Julian birth date and birth mass. Interestingly, environmental factors affected Julian birth date and birth mass even though mothers were continuously allowed access to a high-quality diet. Random effects revealed considerable variation among mothers and years. This study demonstrates that, in long-lived polytocous species, environmental factors may have a greater influence on natal features than previously supposed and the influence from biological factors is also complex. The documented responses to environmental influences provide unique insights into how mammalian seasonal reproductive dynamics may respond to current changes in climate.</p></div

Models analyzed and summaries of model selection for the influence of biological variables (litter type, maternal age, study program), and environmental variables on parturition date (Julian date) of penned white-tailed deer from the Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.

<p>Each model contained predictor variables for litter type (LitType), age of the mother and its quadratic term (MaternalAge), study program (StudyProgram) and environmental predictors for each model. Precipitation and temperature values for each month as well as summed (precipitation) and average (temperature) total from Aug–Jun and Apr–Jun. Precipitation was calculated as the total precipitation in a month (mm). Temperature was calculated as the mean maximum temperature per month (°C). The number of parameters in each model is K, AIC_<i>c</i> is the Akaike value for each model, ΔAIC_<i>c</i> is the change in value compared to the most highly selected model and Weight is the Akaike weight for each model. Models are arranged from highest to lowest Akaike weight.</p><p>Models analyzed and summaries of model selection for the influence of biological variables (litter type, maternal age, study program), and environmental variables on parturition date (Julian date) of penned white-tailed deer from the Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.</p

Sources of variation utilizing restricted maximum likelihood estimation for a linear mixed-effects model assessing the influence of reproductive components on birth mass (kg) of singleton and twin white-tailed deer at Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.

<p>Headers denote the source of variation (SOV), mean squares (MS), degrees of freedom for the numerator and denominator (df_N, df_D), F-test (<i>F</i>), and p-value (<i>P</i>). Sources of variation included LitType (litter types comprised of singleton female and male, twin females and males, and twin mixed litters), MaternalAge and its quadratic term (known age of the mother), and StudyProgram (grouped study programs consisted of StudyProgram1 = 16% protein diet throughout life, StudyProgram2 = sires possessed spike antler characteristics when they were 1.5 years of age, and StudyProgram3 = sires consumed 8% protein diet from 0.5–1.5 years of age and then placed on 16% protein diet for the rest of life). Random effects consisted of dam identification and year of birth.</p><p>Sources of variation utilizing restricted maximum likelihood estimation for a linear mixed-effects model assessing the influence of reproductive components on birth mass (kg) of singleton and twin white-tailed deer at Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.</p

Walter climate diagram derived from a weather station in Kerrville, TX, USA from 1977–2012.

<p>The solid line represents the average total precipitation (mm) and the dashed line represents the average mean temperature for each month.</p

Models analyzed and summaries of model selection for the influence of biological variables (litter type, maternal age, study program), and environmental variables on birth mass (kg) of captive white-tailed deer from the Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.

<p>Each model contained predictor variables for litter type, age of the mother, and study program and added predictors for each model are shown below. Precipitation was calculated as the total precipitation (mm) in a month or range of months. Temperature was calculated as the mean maximum temperature (°C) per month or range of months. Number of parameters in each model is K, AIC_<i>c</i> is the Akaike value for each model, ΔAIC_<i>c</i> is the change in value compared to the most highly selected model and Weight is the Akaike weight for each model. Models are arranged from highest to lowest Akaike weight.</p><p>Models analyzed and summaries of model selection for the influence of biological variables (litter type, maternal age, study program), and environmental variables on birth mass (kg) of captive white-tailed deer from the Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.</p

Sources of variation utilizing restricted maximum likelihood estimation for a linear mixed-effects model assessing the influence of reproductive components on parturition date (Julian date) of singleton and twin white-tailed deer at Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.

<p>Headers denote the source of variation (SOV), mean squares (MS), degrees of freedom for the numerator and denominator (df_N, df_D), F-test (<i>F</i>), and p-value (<i>P</i>). Sources of variation included LitType (litter types comprised of singleton female and male, twin females and males, and twin mixed litters), MaternalAge (known maternal age) and its quadratic term, and StudyProgram (grouped study programs consisted of StudyProgram1 = 16% protein diet throughout life, StudyProgram2 = sires possessed spike antler characteristics when they were 1.5 years of age, and StudyProgram3 = sires consumed 8% protein diet from 0.5–1.5 years of age and then placed on 16% protein diet for the rest of life). Random effects consisted of dam identification and year of birth.</p><p>Sources of variation utilizing restricted maximum likelihood estimation for a linear mixed-effects model assessing the influence of reproductive components on parturition date (Julian date) of singleton and twin white-tailed deer at Kerr Wildlife Management Area, Kerr County, TX from 1977–2012.</p

Predicted values from a linear mixed effect model estimating the birth mass (kg) of captive white-tailed deer at Kerr Wildlife Management Area, Kerr County, Texas, USA from 1977–2012.

<p>Predicted birth mass was estimated across the range of each variable deemed important while controlling for all other variables (variable constants included: Maternal age = 4, Litter type = female singleton, Study program = study program 1, and December temperature = 16.0°C). The solid lines represent the predicted estimate for birth mass and the dashed lines are the standard error envelopes for the estimates. Random effects were treated as categorical variables and included a unique identifier for each mother and the year of birth for each fawn.</p