On the Integration of Medium Wave Infrared Cameras for Vision-Based Navigation

The ubiquitous nature of GPS has fostered its widespread integration of navigation into a variety of applications, both civilian and military. One alternative to ensure continued flight operations in GPS-denied environments is vision-aided navigation, an approach that combines visual cues from a camera with an inertial measurement unit (IMU) to estimate the navigation states of a moving body. The majority of vision-based navigation research has been conducted in the electro-optical (EO) spectrum, which experiences limited operation in certain environments. The aim of this work is to explore how such approaches extend to infrared imaging sensors. In particular, it examines the ability of medium-wave infrared (MWIR) imagery, which is capable of operating at night and with increased vision through smoke, to expand the breadth of operations that can be supported by vision-aided navigation. The experiments presented here are based on the Minor Area Motion Imagery (MAMI) dataset that recorded GPS data, inertial measurements, EO imagery, and MWIR imagery captured during flights over Wright-Patterson Air Force Base. The approach applied here combines inertial measurements with EO position estimates from the structure from motion (SfM) algorithm. Although precision timing was not available for the MWIR imagery, the EO-based results of the scene demonstrate that trajectory estimates from SfM offer a significant increase in navigation accuracy when combined with inertial data over using an IMU alone. Results also demonstrated that MWIR-based positions solutions provide a similar trajectory reconstruction to EO-based solutions for the same scenes. While the MWIR imagery and the IMU could not be combined directly, through comparison to the combined solution using EO data the conclusion here is that MWIR imagery (with its unique phenomenologies) is capable of expanding the operating envelope of vision-aided navigation

A Novel Artificial Organic Controller with Hermite Optical Flow Feedback for Mobile Robot Navigation

This chapter describes a novel nature-inspired and intelligent control system for mobile robot navigation using a fuzzy-molecular inference (FMI) system as the control strategy and a single vision-based sensor device, that is, image acquisition system, as feedback. In particular, FMI system is proposed as a hybrid fuzzy inference system with an artificial hydrocarbon network structure as defuzzifier that deals with uncertainty in motion feedback, improving robot navigation in dynamic environments. Additionally, the robotics system uses processed information from an image acquisition device using a real-time Hermite optical flow approach. This organic and nature-inspired control strategy was compared with a conventional controller and validated in an educational robot platform, providing excellent results when navigating in dynamic environments with a single-constrained perception device

FREQUENCY DOMAIN CHARACTERIZATION OF OPTIC FLOW AND VISION-BASED OCELLAR SENSING FOR ROTATIONAL MOTION

The structure of an animal’s eye is determined by the tasks it must perform. While vertebrates rely on their two eyes for all visual functions, insects have evolved a wide range of specialized visual organs to support behaviors such as prey capture, predator evasion, mate pursuit, flight stabilization, and navigation. Compound eyes and ocelli constitute the vision forming and sensing mechanisms of some flying insects. They provide signals useful for flight stabilization and navigation. In contrast to the well-studied compound eye, the ocelli, seen as the second visual system, sense fast luminance changes and allows for fast visual processing. Using a luminance-based sensor that mimics the insect ocelli and a camera-based motion detection system, a frequency-domain characterization of an ocellar sensor and optic flow (due to rotational motion) are analyzed. Inspired by the insect neurons that make use of signals from both vision sensing mechanisms, advantages, disadvantages and complementary properties of ocellar and optic flow estimates are discussed

SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-Oriented Navigation System

Both animals and mobile robots, or animats, need adaptive control systems to guide their movements through a novel environment. Such control systems need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once the environment is familiar. How reactive and planned behaviors interact together in real time, and arc released at the appropriate times, during autonomous navigation remains a major unsolved problern. This work presents an end-to-end model to address this problem, named SOVEREIGN: A Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation system. The model comprises several interacting subsystems, governed by systems of nonlinear differential equations. As the animat explores the environment, a vision module processes visual inputs using networks that arc sensitive to visual form and motion. Targets processed within the visual form system arc categorized by real-time incremental learning. Simultaneously, visual target position is computed with respect to the animat's body. Estimates of target position activate a motor system to initiate approach movements toward the target. Motion cues from animat locomotion can elicit orienting head or camera movements to bring a never target into view. Approach and orienting movements arc alternately performed during animat navigation. Cumulative estimates of each movement, based on both visual and proprioceptive cues, arc stored within a motor working memory. Sensory cues are stored in a parallel sensory working memory. These working memories trigger learning of sensory and motor sequence chunks, which together control planned movements. Effective chunk combinations arc selectively enhanced via reinforcement learning when the animat is rewarded. The planning chunks effect a gradual transition from reactive to planned behavior. The model can read-out different motor sequences under different motivational states and learns more efficient paths to rewarded goals as exploration proceeds. Several volitional signals automatically gate the interactions between model subsystems at appropriate times. A 3-D visual simulation environment reproduces the animat's sensory experiences as it moves through a simplified spatial environment. The SOVEREIGN model exhibits robust goal-oriented learning of sequential motor behaviors. Its biomimctic structure explicates a number of brain processes which are involved in spatial navigation.Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-01-1-0397); National Science Foundation (IRI 90-24877, SBE-0354378); Office of Naval Research (N00014-91-J-4100, N00014-92-J-1309, N00014-95-1-0657, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

Exploring Motion Signatures for Vision-Based Tracking, Recognition and Navigation

As cameras become more and more popular in intelligent systems, algorithms and systems for understanding video data become more and more important. There is a broad range of applications, including object detection, tracking, scene understanding, and robot navigation. Besides the stationary information, video data contains rich motion information of the environment. Biological visual systems, like human and animal eyes, are very sensitive to the motion information. This inspires active research on vision-based motion analysis in recent years. The main focus of motion analysis has been on low level motion representations of pixels and image regions. However, the motion signatures can benefit a broader range of applications if further in-depth analysis techniques are developed. In this dissertation, we mainly discuss how to exploit motion signatures to solve problems in two applications: object recognition and robot navigation. First, we use bird species recognition as the application to explore motion signatures for object recognition. We begin with study of the periodic wingbeat motion of flying birds. To analyze the wing motion of a flying bird, we establish kinematics models for bird wings, and obtain wingbeat periodicity in image frames after the perspective projection. Time series of salient extremities on bird images are extracted, and the wingbeat frequency is acquired for species classification. Physical experiments show that the frequency based recognition method is robust to segmentation errors and measurement lost up to 30%. In addition to the wing motion, the body motion of the bird is also analyzed to extract the flying velocity in 3D space. An interacting multi-model approach is then designed to capture the combined object motion patterns and different environment conditions. The proposed systems and algorithms are tested in physical experiments, and the results show a false positive rate of around 20% with a low false negative rate close to zero. Second, we explore motion signatures for vision-based vehicle navigation. We discover that motion vectors (MVs) encoded in Moving Picture Experts Group (MPEG) videos provide rich information of the motion in the environment, which can be used to reconstruct the vehicle ego-motion and the structure of the scene. However, MVs suffer from high noise level. To handle the challenge, an error propagation model for MVs is first proposed. Several steps, including MV merging, plane-at-infinity elimination, and planar region extraction, are designed to further reduce noises. The extracted planes are used as landmarks in an extended Kalman filter (EKF) for simultaneous localization and mapping. Results show that the algorithm performs localization and plane mapping with a relative trajectory error below 5:1%. Exploiting the fact that MVs encodes both environment information and moving obstacles, we further propose to track moving objects at the same time of localization and mapping. This enables the two critical navigation functionalities, localization and obstacle avoidance, to be performed in a single framework. MVs are labeled as stationary or moving according to their consistency to geometric constraints. Therefore, the extracted planes are separated into moving objects and the stationary scene. Multiple EKFs are used to track the static scene and the moving objects simultaneously. In physical experiments, we show a detection rate of moving objects at 96:6% and a mean absolute localization error below 3:5 meters

A factorization approach to inertial affine structure from motion

We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives

A factorization approach to inertial affine structure from motion

We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives