Towards Visual Ego-motion Learning in Robots

Many model-based Visual Odometry (VO) algorithms have been proposed in the past decade, often restricted to the type of camera optics, or the underlying motion manifold observed. We envision robots to be able to learn and perform these tasks, in a minimally supervised setting, as they gain more experience. To this end, we propose a fully trainable solution to visual ego-motion estimation for varied camera optics. We propose a visual ego-motion learning architecture that maps observed optical flow vectors to an ego-motion density estimate via a Mixture Density Network (MDN). By modeling the architecture as a Conditional Variational Autoencoder (C-VAE), our model is able to provide introspective reasoning and prediction for ego-motion induced scene-flow. Additionally, our proposed model is especially amenable to bootstrapped ego-motion learning in robots where the supervision in ego-motion estimation for a particular camera sensor can be obtained from standard navigation-based sensor fusion strategies (GPS/INS and wheel-odometry fusion). Through experiments, we show the utility of our proposed approach in enabling the concept of self-supervised learning for visual ego-motion estimation in autonomous robots.Comment: Conference paper; Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2017, Vancouver CA; 8 pages, 8 figures, 2 table

High-Performance and Tunable Stereo Reconstruction

Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles.Comment: Accepted to International Conference on Robotics and Automation (ICRA) 2016; 8 pages, 5 figure

H2S paper strip method - A bacteriological test for faecal coliforms in drinking water at various temperatures

Epidemics arising from waterborne diseases are a global health problem. Faecal contamination of drinking water is the main cause of these outbreaks. According to WHO (1996) for drinking water to be safe, a 100 ml sample should not contain any coliform bacteria. The standard methods currently used for routine testing have many limitations especially when applied in remote areas. The H2S method has been developed as an on-site, inexpensive and easy to use method to test drinking water for remote and rural areas. The present work analyses the reliability of the H2S method for detecting faecal contamination in drinking water. The minimum level of faecal coliforms that could be detected and the incubation period required at various levels of contamination were studied. The range of temperatures at which the method was effective and the incubation period required at various temperatures were also determined. The H2S method was found to be able to detect contamination down to a level of 1 CFU/100 ml of coliform bacteria. Although the H2S method could be used at a temperature range of 20 to 44oC, temperatures between 28 to 37oC gave faster results. An incubation period of only 24 hours was required at 37oC, which was found to be the most suitable incubation temperature. The incubation period increased with a decrease or increase in temperature

Poles and zeros – examples of the behavioral approach applied to discrete linear repetitive processes

In this paper the behavorial approach is applied to discrete linear repetitive processes, which are class of 2D systems of both systems theoretic and applications interest. The main results are on poles and zeros for these processes, which have exponential trajectory interpretations

A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit

We describe a new MCMC method optimized for the sampling of probability measures on Hilbert space which have a density with respect to a Gaussian; such measures arise in the Bayesian approach to inverse problems, and in conditioned diffusions. Our algorithm is based on two key design principles: (i) algorithms which are well-defined in infinite dimensions result in methods which do not suffer from the curse of dimensionality when they are applied to approximations of the infinite dimensional target measure on \bbR^N; (ii) non-reversible algorithms can have better mixing properties compared to their reversible counterparts. The method we introduce is based on the hybrid Monte Carlo algorithm, tailored to incorporate these two design principles. The main result of this paper states that the new algorithm, appropriately rescaled, converges weakly to a second order Langevin diffusion on Hilbert space; as a consequence the algorithm explores the approximate target measures on \bbR^N in a number of steps which is independent of $N$. We also present the underlying theory for the limiting non-reversible diffusion on Hilbert space, including characterization of the invariant measure, and we describe numerical simulations demonstrating that the proposed method has favourable mixing properties as an MCMC algorithm.Comment: 41 pages, 2 figures. This is the final version, with more comments and an extra appendix adde

A possible observational bias in the estimation of the virial parameter in virialized clumps

The dynamics of massive clumps, the environment where massive stars originate, is still unclear. Many theories predict that these regions are in a state of near-virial equilibrium, or near energy equi-partition, while others predict that clumps are in a sub-virial state. Observationally, the majority of the massive clumps are in a sub-virial state with a clear anti-correlation between the virial parameter $\alpha_{vir}$ and the mass of the clumps $M_{c}$, which suggests that the more massive objects are also the more gravitationally bound. Although this trend is observed at all scales, from massive clouds down to star-forming cores, theories do not predict it. In this work we show how, starting from virialized clumps, an observational bias is introduced in the specific case where the kinetic and the gravitational energies are estimated in different volumes within clumps and how it can contribute to the spurious $\alpha_{vir}-M_{c}$ anti-correlation in these data. As a result, the observed effective virial parameter $\tilde{\alpha}_{eff}<\alpha_{vir}$, and in some circumstances it might not be representative of the virial state of the observed clumps.Comment: A&A letter, accepte

Sexual Differentiation of Circadian Clock Function in the Adrenal Gland

Sex differences in glucocorticoid production are associated with increased responsiveness of the adrenal gland in females. However, the adrenal-intrinsic mechanisms that establish sexual dimorphic function remain ill defined. Glucocorticoid production is gated at the molecular level by the circadian clock, which may contribute to sexual dimorphic adrenal function. Here we examine sex differences in the adrenal gland using an optical reporter of circadian clock function. Adrenal glands were cultured from male and female Period2::Luciferase (PER2::LUC) mice to assess clock function in vitro in real time. We confirm that there is a pronounced sex difference in the intrinsic capacity to sustain PER2::LUC rhythms in vitro, with higher amplitude rhythms in adrenal glands collected from males than from females. Changes in adrenal PER2::LUC rhythms over the reproductive life span implicate T as an important factor in driving sex differences in adrenal clock function. By directly manipulating hormone levels in adult mice in vivo, we demonstrate that T increases the amplitude of PER2::LUC rhythms in adrenal glands of both male and female mice. In contrast, we find little evidence that ovarian hormones modify adrenal clock function. Lastly, we find that T in vitro can increase the amplitude of PER2::LUC rhythms in male adrenals but not female adrenals, which suggests the existence of sex differences in the mechanisms of T action in vivo. Collectively these results reveal that activational effects of T alter circadian timekeeping in the adrenal gland, which may have implications for sex differences in stress reactivity and stress-related disorders

Seasonal and spatial distribution of phytoplankters in Cochin backwater

The observations made on the phytoplankton of Cochin Backwater for a period of one year at seven selected stations have been discussed. There is considerable seasonal and spat ial variation of phytopplankters both in magnitude and composition. During the pre-monsoon period, the pbytoplankters were chiefly constituted by species of diatoms