A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new goals, and (2) data inefficiency, i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization. To address the second issue, we propose the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently. We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), (4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.

Target-driven visual navigation in indoor scenes using deep reinforcement learning

Simulation and the monte carlo method

Autonomous underwater vehicle (AUV) navigation and localization in underwater environments is particularly challenging due to the rapid attenuation of Global Positioning System (GPS) and radio-frequency signals. Underwater communications are low bandwidth and unreliable, and there is no access to a global positioning system. Past approaches to solve the AUV localization problem have employed expensive inertial sensors, used installed beacons in the region of interest, or required periodic surfacing of the AUV. While these methods are useful, their performance is fundamentally limited. Advances in underwater communications and the application of simultaneous localization and mapping (SLAM) technology to the underwater realm have yielded new possibilities in the field. This paper presents a review of the state of the art of AUV navigation and localization, as well as a description of some of the more commonly used methods. In addition, we highlight areas of future research potential.

/pdf/auv-navigation-and-localization-a-review-2aaowbjv0i.pdf

AUV Navigation and Localization: A Review

Mobile robot vision-based navigation has been the source of countless research contributions, from the domains of both vision and control. Vision is becoming more and more common in applications such as localization, automatic map construction, autonomous navigation, path following, inspection, monitoring or risky situation detection. This survey presents those pieces of work, from the nineties until nowadays, which constitute a wide progress in visual navigation techniques for land, aerial and autonomous underwater vehicles. The paper deals with two major approaches: map-based navigation and mapless navigation. Map-based navigation has been in turn subdivided in metric map-based navigation and topological map-based navigation. Our outline to mapless navigation includes reactive techniques based on qualitative characteristics extraction, appearance-based localization, optical flow, features tracking, plane ground detection/tracking, etc... The recent concept of visual sonar has also been revised.

Visual Navigation for Mobile Robots: A Survey

Autonomous Underwater Vehicles (AUVs) operate in ocean environments characterized by complex spatial variability which can jeopardize their missions. To avoid this, planning safety routes with minimum energy cost is of primary importance. This work explores the benefits, in terms of energy cost, of path planning in marine environments showing certain spatial variability. Extensive computations have been carried out to calculate, by means of an A* search procedure, optimal paths on ocean environments with different length scale of eddies and different current intensities. To get statistical confidence, different realizations of the eddy field and starting-ending points of the path have been considered for each environment. Unlike previous works, the more realistic and applied case of constant thrust power navigation is considered. Results indicate that substantial energy savings of planned paths compared to straight line trajectories are obtained when the current intensity of the eddy structures and the vehicle speed are comparable. Conversely, the straight line path between starting and ending points can be considered an optimum path when the current speed does not exceed half of the vehicle velocity. In both situations, benefits of path planning seem dependent of the spatial structure of the eddy field.

Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

ReviewIntervention AUVs: The next challenge☆☆☆

Nowadays, the surveillance and inspection of underwater installations, such as power and telecommunication cables and pipelines, is carried out by operators that, being on the surface, drive a remotely operated vehicle (ROV) with cameras mounted over it. This is a tedious and high time-consuming task, easily prone to errors mainly because of loss of attention or fatigue of the human operator. Besides, the complexity of the task is increased by the lack of quality of typical seabed images, which are mainly characterised by blurring, non-uniform illumination, lack of contrast and instability in the vehicle motion. In this study, the development of a vision system guiding an autonomous underwater vehicle (AUV) able to detect and track automatically an underwater power cable laid on the seabed is the main concern. The vision system that is proposed tracks the cable with an average success rate above 90%. The system has been tested using sequences coming from a video tape obtained in several tracking sessions of various real cables with a ROV driven from the surface. These cables were installed several years ago, so that the images do not present highly contrasted cables over a salady seabed; on the contrary, these cables are partially covered in algae or sand, and are surrounded by other algae and rocks, thus making the sequences highly realistic.

A vision system for an underwater cable tracker

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/2819.pdf

Gabriel Oliver

Papers

Visual Navigation for Mobile Robots: A Survey

Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

ReviewIntervention AUVs: The next challenge☆☆☆

A vision system for an underwater cable tracker

Intervention AUVs: The Next Challenge