Visual Odometry Estimation Using Selective Features

The rapid growth in computational power and technology has enabled the automotive industry to do extensive research into autonomous vehicles. So called self- driven cars are seen everywhere, being developed from many companies like, Google, Mercedes Benz, Delphi, Tesla, Uber and many others. One of the challenging tasks for these vehicles is to track incremental motion in runtime and to analyze surroundings for accurate localization. This crucial information is used by many internal systems like active suspension control, autonomous steering, lane change assist and many such applications. All these systems rely on incremental motion to infer logical conclusions. Measurement of incremental change in pose or perspective, in other words, changes in motion, measured using visual only information is called Visual Odometry. This thesis proposes an approach to solve the Visual Odometry problem by using stereo-camera vision to incrementally estimate the pose of a vehicle by examining changes that motion induces on the background in the frame captured from stereo cameras. The approach in this thesis research uses a selective feature based motion tracking method to track the motion of the vehicle by analyzing the motion of its static surroundings and discarding the motion induced by dynamic background (outliers). The proposed approach considers that the surrounding may have moving objects like a truck, a car or a pedestrian body which has its own motion which may be different with respect to the vehicle. Use of stereo camera adds depth information which provides more crucial information necessary for detecting and rejecting outliers. Refining the interest point location using sinusoidal interpolation further increases the accuracy of the motion estimation results. The results show that by using a process that chooses features only on the static background and by tracking these features accurately, robust semantic information can be obtained

Learning, Moving, And Predicting With Global Motion Representations

In order to effectively respond to and influence the world they inhabit, animals and other intelligent agents must understand and predict the state of the world and its dynamics. An agent that can characterize how the world moves is better equipped to engage it. Current methods of motion computation rely on local representations of motion (such as optical flow) or simple, rigid global representations (such as camera motion). These methods are useful, but they are difficult to estimate reliably and limited in their applicability to real-world settings, where agents frequently must reason about complex, highly nonrigid motion over long time horizons. In this dissertation, I present methods developed with the goal of building more flexible and powerful notions of motion needed by agents facing the challenges of a dynamic, nonrigid world. This work is organized around a view of motion as a global phenomenon that is not adequately addressed by local or low-level descriptions, but that is best understood when analyzed at the level of whole images and scenes. I develop methods to: (i) robustly estimate camera motion from noisy optical flow estimates by exploiting the global, statistical relationship between the optical flow field and camera motion under projective geometry; (ii) learn representations of visual motion directly from unlabeled image sequences using learning rules derived from a formulation of image transformation in terms of its group properties; (iii) predict future frames of a video by learning a joint representation of the instantaneous state of the visual world and its motion, using a view of motion as transformations of world state. I situate this work in the broader context of ongoing computational and biological investigations into the problem of estimating motion for intelligent perception and action

Bioinspired engineering of exploration systems for NASA and DoD

A new approach called bioinspired engineering of exploration systems (BEES) and its value for solving pressing NASA and DoD needs are described. Insects (for example honeybees and dragonflies) cope remarkably well with their world, despite possessing a brain containing less than 0.01% as many neurons as the human brain. Although most insects have immobile eyes with fixed focus optics and lack stereo vision, they use a number of ingenious, computationally simple strategies for perceiving their world in three dimensions and navigating successfully within it. We are distilling selected insect-inspired strategies to obtain novel solutions for navigation, hazard avoidance, altitude hold, stable flight, terrain following, and gentle deployment of payload. Such functionality provides potential solutions for future autonomous robotic space and planetary explorers. A BEES approach to developing lightweight low-power autonomous flight systems should be useful for flight control of such biomorphic flyers for both NASA and DoD needs. Recent biological studies of mammalian retinas confirm that representations of multiple features of the visual world are systematically parsed and processed in parallel. Features are mapped to a stack of cellular strata within the retina. Each of these representations can be efficiently modeled in semiconductor cellular nonlinear network (CNN) chips. We describe recent breakthroughs in exploring the feasibility of the unique blending of insect strategies of navigation with mammalian visual search, pattern recognition, and image understanding into hybrid biomorphic flyers for future planetary and terrestrial applications. We describe a few future mission scenarios for Mars exploration, uniquely enabled by these newly developed biomorphic flyers

An On-Board Visual-Based Attitude Estimation System For Unmanned Aerial Vehicle Mapping

This paper evaluates the performances of several salient feature detectors, namely; Harris detector, Minimum Eigenvalue (MinEig), Scale Invariant Feature Transform (SIFT), Maximally Stable Extremal Region (MSER), Speeded Up Robust Feature (SURF), Features from Accelerated Segment Test (FAST), and Binary Robust Scale Invariant Keypoint (BRISK), in order to assess the suitability in the application of the proposed visual-based attitude estimation system. Throughout the experiment, three main requirements have been investigated which include Time-to-Complete (TTC), detection rate, and matching rate. It was found that SURF fulfills each of the system’s requirements. Moreover, it was also found that keypoints detection capabilities affect the processing time, and the clustering patterns in the results may assist in automated inspection of correct and false matching

Depth map from the combination of matched points with active contours

IEEE Intelligent Vehicles Symposium (IVS), 2000, Dearborn (EE.UU.)This paper describes the analysis of an active contour fitted to a target in a sequence of images recorded by a freely moving uncalibrated camera. The motivating application is the visual guidance of a robot towards a target. Contour deformations are analysed to extract the scaled depth of the target, and to explore the feasibility of 3D egomotion recovery. The scaled depth is used to compute the time to contact, which provides a measure of distance to the target, and also to improve the common depth maps obtained from point matches, which are a valuable input for the robot to avoid obstacles.This work was supported by the project 'Navegación basada en visión de robots autónomos en entornos no estructurados.' (070-724).Peer Reviewe

3D object reconstruction using stereo and motion

The extraction of reliable range data from images is investigated, considering, as a possible solution, the integration of different sensor modalities. Two different algorithms are used to obtain independent estimates of depth from a sequence of stereo images. The results are integrated on the basis of the uncertainty of each measure. The stereo algorithm uses a coarse-to-fine control strategy to compute disparity. An algorithm for depth-from-motion is used, exploiting the constraint imposed by active motion of the cameras. To obtain a 3D description of the objects, the motion of the cameras is purposefully controlled, in such a manner as to move around the objects in view while the gaze is directed toward a fixed point in space. This egomotion strategy, which is similar to that adopted by the human visuomotor system, allows a better exploration of partially occluded objects and simplifies the motion equations. When tested on real scenes, the algorithm demonstrated a low sensitivity to image noise, mainly due to the integration of independent measures. An experiment performed on a real scene containing several objects is presented