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

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Lunar sourcebook. A user's guide to the moon

https://people.kth.se/~dimos/DualSurvey.pdf

Short survey: Dual arm manipulation-A survey

Application of robotics in offshore oil and gas industry- A review Part II

The inspection of marine vessels is currently performed manually Inspectors use sensors (eg cameras, devices for non-destructive testing) to detect damaged areas, cracks, and corrosion in large cargo holds, tanks, and other parts of a ship Due to the size and complex geometry of most ships, ship inspection is time-consuming and expensive The EU-funded project MINOAS develops concepts for a Marine Inspection Robotic Assistant System to improve and to automate ship inspections As one example of a physical system implementation, we introduce our magnetic wall-climbing robot This remotely operated lightweight system is able to climb a vessels steel frame and is able to deliver visual inspection data on-line For any type of surveying process, the raw and meta data are mandatory in order to compare inspection data over time In this paper, we describe our approach of how the magnetic climbing robot is localized and controlled Additionally, we describe the design of the robot as well as the localization device which is able to provide a real-time 3D pose of the inspection robot

Design and control of a lightweight magnetic climbing robot for vessel inspection

For disaster mitigation as well as for urban search and rescue (USAR) missions, it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. Robots are predestined to this task, but the requirements for such mobile systems are demanding. They should be quick and agile and, at the same time, be able to deal with rough terrain and even to climb stairs. The latter is always required if the rescue personnel has to get access to higher floors inside a building. A rugged, waterproof and dust-proof corpus, and, if possible, the ability to swim, are only a few of many requirements for such robots. With those requirements in mind, the hybrid legged-wheeled robot ASGUARD was developed. This robot is able to cope with stairs, very rough terrain, and is able to move fast on flat ground. We will describe a versatile adaptive controller, based only on proprioceptive data. An additional inclination feedback is used to make the controller versatile for flat ground as well as for steep slopes and stairs. An attachable float provided, the robot is able to swim, using the same locomotion approach. By using twenty compliant legs, which are mounted around four individually rotating hip-shafts, we use an abstract model of quadruped locomotion. For the control design, four independent pattern generators are used. In contrast to many other hybrid legged-wheeled robots, we use the direct proprioceptive feedback in order to modify the internal control loop, thus adapting the model of the motion pattern. For difficult terrains, like slopes and stairs, we use a phase-adaptive approach which is using directly the proprioceptive data from the legs.

A Versatile Stair-Climbing Robot for Search and Rescue Applications

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems — including perception and manipulation — instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.

The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Modern SLAM systems with a depth sensor are able to reliably reconstruct dense 3D geometric maps of indoor scenes. Representing these maps in terms of meaningful entities is a step towards building semantic maps for autonomous robots. One approach is to segment the 3D maps into semantic objects using Conditional Random Fields (CRF), which requires large 3D ground truth datasets to train the classification model. Additionally, the CRF inference is often computationally expensive. In this paper, we present an unsupervised geometric-based approach for the segmentation of 3D point clouds into objects and meaningful scene structures. We approximate an input point cloud by an adjacency graph over surface patches, whose edges are then classified as being either on or off. We devise an effective classifier which utilises both global planar surfaces and local surface convexities for edge classification. More importantly, we propose a novel global plane extraction algorithm for robustly discovering the underlying planes in the scene. Our algorithm is able to enforce the extracted planes to be mutually orthogonal or parallel which conforms usually with human-made indoor environments. We reconstruct 654 3D indoor scenes from NYUv2 sequences to validate the efficiency and effectiveness of our segmentation method.

Geometrically consistent plane extraction for dense indoor 3D maps segmentation

Seagoing vessels have to undergo regular inspections, which are currently performed manually by ship surveyors. The main cost factor in a ship inspection is to provide access to the different areas of the ship, since the surveyor has to be close to the inspected parts, usually within arm's reach, either to perform a visual analysis or to take thickness measurements. The access to the structural elements in cargo holds, e.g., bulkheads, is normally provided by staging or by "cherry-picking" cranes. To make ship inspections safer and more cost-efficient, we have introduced new inspection methods, tools, and systems, which have been evaluated in field trials, particularly focusing on cargo holds. More precisely, two magnetic climbing robots and a micro-aerial vehicle, which are able to assist the surveyor during the inspection, are introduced. Since localization of inspection data is mandatory for the surveyor, we also introduce an external localization system that has been verified in field trials, using a climbing inspection robot. Furthermore, the inspection data collected by the robotic systems are organized and handled by a spatial content management system that enables us to compare the inspection data of one survey with those from another, as well as to document the ship inspection when the robot team is used. Image-based defect detection is addressed by proposing an integrated solution for detecting corrosion and cracks. The systems' performance is reported, as well as conclusions on their usability, all in accordance with the output of field trials performed onboard two different vessels under real inspection conditions.

Markus Eich

Papers

Design and control of a lightweight magnetic climbing robot for vessel inspection

A Versatile Stair-Climbing Robot for Search and Rescue Applications

The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research

Geometrically consistent plane extraction for dense indoor 3D maps segmentation

A Robot Application for Marine Vessel Inspection