A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

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

A Self Organization-Based Optical Flow Estimator with GPU Implementation

This work describes a parallelizable optical flow estimator that uses a modified batch version of the Self Organizing Map (SOM). This gradient-based estimator handles the ill-posedness in motion estimation via a novel combination of regression and a self organization strategy. The aperture problem is explicitly modeled using an algebraic framework that partitions motion estimates obtained from regression into two sets, one (set Hc) with estimates with high confidence and another (set Hp) with low confidence estimates. The self organization step uses a uniquely designed pair of training set (Q=Hc) and the initial weights set (W=Hc U Hp). It is shown that with this specific choice of training and initial weights sets, the interpolation of flow vectors is achieved primarily due to the regularization property of SOM. Moreover, the computationally involved step of finding the winner unit in SOM simplifies to indexing into a 2D array making the algorithm parallelizable and highly scalable. To preserve flow discontinuities at occlusion boundaries, we have designed anisotropic neighborhood function for SOM that uses a novel OFCE residual-based distance measure. A multi-resolution or pyramidal approach is used to estimate large motion. As the algorithm is scalable, with sufficient number of computing cores (for example on a GPU), the implementation of the estimator can be made real-time. With the available true motion from Middlebury database, error metrics are computed

Dynamic Body VSLAM with Semantic Constraints

Image based reconstruction of urban environments is a challenging problem that deals with optimization of large number of variables, and has several sources of errors like the presence of dynamic objects. Since most large scale approaches make the assumption of observing static scenes, dynamic objects are relegated to the noise modeling section of such systems. This is an approach of convenience since the RANSAC based framework used to compute most multiview geometric quantities for static scenes naturally confine dynamic objects to the class of outlier measurements. However, reconstructing dynamic objects along with the static environment helps us get a complete picture of an urban environment. Such understanding can then be used for important robotic tasks like path planning for autonomous navigation, obstacle tracking and avoidance, and other areas. In this paper, we propose a system for robust SLAM that works in both static and dynamic environments. To overcome the challenge of dynamic objects in the scene, we propose a new model to incorporate semantic constraints into the reconstruction algorithm. While some of these constraints are based on multi-layered dense CRFs trained over appearance as well as motion cues, other proposed constraints can be expressed as additional terms in the bundle adjustment optimization process that does iterative refinement of 3D structure and camera / object motion trajectories. We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth. We are able to show average relative error reduction by a significant amount for moving object trajectory reconstruction relative to state-of-the-art methods like VISO 2, as well as standard bundle adjustment algorithms

Egomotion from event-based SNN optical flow

We present a method for computing egomotion using event cameras with a pre-trained optical flow spiking neural network (SNN). To address the aperture problem encountered in the sparse and noisy normal flow of the initial SNN layers, our method includes a sliding-window bin-based pooling layer that computes a fused full flow estimate. To add robustness to noisy flow estimates, instead of computing the egomotion from vector averages, our method optimizes the intersection of constraints. The method also includes a RANSAC step to robustly deal with outlier flow estimates in the pooling layer. We validate our approach on both simulated and real scenes and compare our results favorably to the state-of-the-art methods. However, our method may be sensitive to datasets and motion speeds different from those used for training, limiting its generalizability.This work received support from projects EBCON (PID2020-119244GBI00) and AUDEL (TED2021-131759A-I00) funded by MCIN/ AEI/ 10.13039/ 501100011033 and by the "European Union NextGenerationEU/PRTR"; the Consolidated Research Group RAIG (2021 SGR 00510) of the Departament de Recerca i Universitats de la Generalitat de Catalunya; and by an FI AGAUR PhD grant to Yi Tian.Peer ReviewedPostprint (author's final draft