pinecone/core-2020-05-10-deduplication huggingface dataset:
The Malta cistern mapping project : underwater robot mapping and localization within ancient tunnel systems

This paper documents the development of an underwater robot system enabled with several mapping and\ud
localization techniques applied to a particular archaeological expedition. The goal of the expedition was to\ud
explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted\ud
\ud
as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of sev-\ud
eral connected chambers, still containing water. A sonar-equipped remotely operated vehicle (ROV) was de-\ud
ployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping\ud
\ud
and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans,\ud
\ud
(2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous lo-\ud
calization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans,\ud
\ud
(5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether\ud
position data. Top-down-view maps of 22 different cisterns were successfully constructed. It is estimated that\ud
the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed\ud
before the expedition.peer-reviewe
Unified command and control for heterogeneous marine sensing networks

Successful command and control (C2) of autonomous vehicles poses challenges that are unique to the marine environment, primarily highly restrictive acoustic communications throughput. To address this, the Unified C2 architecture presented here uses a highly compressed short message encoding scheme (Dynamic Compact Control Language or DCCL) to transfer commands and receive vehicle status. DCCL is readily reconfigurable to provide the flexibility needed to change commands on short notice. Furthermore, operation of multiple types of vehicles requires a C2 architecture that is both scalable and flexible to differences among platform hardware and abilities. The Unified C2 architecture uses the MOOS-IvP autonomy system to act as a “backseat driver” of the vehicle. This provides a uniform interface to the control system on all the vehicles. Also, a hierarchical configuration system is used to allow single changes in configuration to propagate to all vehicles in operation. Status data from all vehicles are displayed visually using Google Earth, which also allows a rapid meshing of data from other sources (sensors, automatic identification system, radar, satellites) from within, as well as outside of, the MOOS-IvP architecture. Results are presented throughout from the CCLNET08, SQUINT08, GLINT08, GLINT09, SWAMSI09, and DURIP09 experiments involving robotic marine autonomous surface craft (ASCs) and Bluefin, OceanServer, and NATO Undersea Research Centre (NURC) autonomous underwater vehicles (AUVs).United States. Office of Naval Research (Grant N00014-1-08-1-0013)United States. Office of Naval Research (Grant N00014-1-08-1-0011
Simple yet stable bearing-only navigation

This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust and easy to implement and does not require sensor calibration or structured environment, and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only. The basic idea is to utilize a monocular vision to correct only the robot's heading, leaving distance measurements to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging. The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim is stated that for closed polygonal trajectories, the position error of this type of navigation does not diverge. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths more than 1 km long
Airborne vision-based collision-detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and TCAS). This paper describes the development and evaluation of a real-time vision-based collision detection system suitable for fixed-wing aerial robotics. Using two fixed-wing UAVs to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units found on commercially-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1024 by 768 pixel image frames at a rate of approximately 30Hz. Flight trials using manned Cessna aircraft where all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms
Fixed-wing attitude estimation using temporal tracking of the horizon and optical flow

In this paper, a method has been developed for estimating pitch angle, roll angle and aircraft body rates based on horizon detection and temporal tracking using a forward-looking camera, without assistance from other sensors. Using an image processing front-end, we select several lines in an image that may or may not correspond to the true horizon. The optical flow at each candidate line is calculated, which may be used to measure the body rates of the aircraft. Using an Extended Kalman Filter (EKF), the aircraft state is propagated using a motion model and a candidate horizon line is  associated using a statistical test based on the optical flow measurements and the location of the horizon. Once associated, the selected horizon line, along with the associated optical flow, is used as a  measurement to the EKF.\ud
\ud
To test the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42◦ and 0.71◦ respectively when compared with a truth attitude source. The Cessna flight resulted in pitch and roll error standard deviations of 1.79◦ and 1.75◦ respectively. The benefits of selecting and tracking the horizon using a motion model and optical flow rather than naively relying on the image processing front-end is also demonstrated
Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration\ud
of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype\ud
spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high\ud
or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of\ud
this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was\ud
followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated\ud
airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo\ud
to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint\ud
or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged\ud
during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and\ud
spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect\ud
new incursions of spore
Toward automated power line corridor monitoring using advanced aircraft control and multisource feature fusion

The conventional manual power line corridor inspection processes that are used by most energy utilities are labor-intensive, time consuming and expensive. Remote sensing technologies represent an attractive and cost-effective alternative approach to these monitoring activities. This paper presents a comprehensive investigation into automated remote sensing based power line corridor monitoring, focusing on recent innovations in the area of increased automation of fixed-wing platforms for aerial data collection, and automated data processing for object recognition using a feature fusion process. Airborne automation is achieved by using a novel approach that provides improved lateral control for tracking corridors and automatic real-time dynamic turning for flying between corridor segments, we call this approach PTAGS. Improved object recognition is achieved by fusing information from multi-sensor (LiDAR and imagery) data and multiple visual feature descriptors (color and texture). The results from our experiments and field survey illustrate the effectiveness of the proposed aircraft control and feature fusion approaches
Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc
Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions
Experiments on Surface Reconstruction for Partially Submerged Marine Structures

Over the past 10 years, significant scientific effort has been dedicated to the problem of three-dimensional (3-D) surface reconstruction for structural systems. However, the critical area of marine structures remains insufficiently studied. The research presented here focuses on the problem of 3-D surface reconstruction in the marine environment. This paper summarizes our hardware, software, and experimental contributions on surface reconstruction over the past few years (2008–2011). We propose the use of off-the-shelf sensors and a robotic platform to scan marine structures both above and below the waterline, and we develop a method and software system that uses the Ball Pivoting Algorithm (BPA) and the Poisson reconstruction algorithm to reconstruct 3-D surface models of marine structures from the scanned data. We have tested our hardware and software systems extensively in Singapore waters, including operating in rough waters, where water currents are around 1–2 m/s. We present results on construction of various 3-D models of marine structures, including slowly moving structures such as floating platforms, moving boats, and stationary jetties. Furthermore, the proposed surface reconstruction algorithm makes no use of any navigation sensor such as GPS, a Doppler velocity log, or an inertial navigation system.Singapore-MIT Alliance for Research and Technology. Center for Environmental Sensing and Modelin
Parameter Identification of a Planetary Rover Wheel–Soil Contact Model via a Bayesian Approach

Soft soil contact models developed for planetary exploration rovers play an important role\ud
in the study of rover mobility. Nowadays, most of the existing contact models are based\ud
on Bekker theory which requires the evaluation of several soil parameters usually measured\ud
via bevameter tests. However, substantial di�erences existing between the plate-soil contact\ud
scenario and the wheel-soil contact scenario, along with large variability associated with the\ud
bevameter experiments, give rise to large uncertainty in the choice of the model parameter\ud
values. In this paper a Bayesian procedure is proposed to deal e�ectively with the presence\ud
of uncertainty. In the proposed approach model parameters are random variables with\ud
prior distributions derived from bevameter measurements. The prior distributions are then\ud
enhanced to posterior distributions through single wheel test data. At the end the procedure\ud
identi�es a set of possible model parameter con�gurations that result in high experimentalnumerical\ud
matching
Automatic driving on ill-defined roads: an adaptive, shape-constrained, color-based method

Ososinski, M., Labrosse, F. (2015). Automatic driving on ill-defined roads: an adaptive, shape-constrained, color-based method. Journal of Field Robotics, 32 (4), 504-533.Autonomous following of ill-defined roads is an important part of visual navigation systems. This paper presents an adaptive method that uses a statistical model of the color of the road surface within a trapezoidal shape that approximately corresponds to the projection of the road on the image plane. The method does not perform an explicit segmentation of the images but instead expands the shape sideways until the match between shape and road worsens, simultaneously computing the color statistics. Results show that the method is capable of reactively following roads, at driving speeds typical of the robots used, in a variety of situations while coping with variable conditions of the road such as surface type, puddles, and shadows. We extensively evaluate the proposed method using a large number of datasets with ground truth (available from http://www.aber.ac.uk/en/cs/research/ir/dss/). We moreover evaluate many color spaces in the context of road following, and we find that the color spaces that separate luminance from color information perform best, especially if the luminance information is discarded.authorsversionPeer reviewe
Vision-based simultaneous localization and mapping in changing outdoor environments

For robots operating in outdoor environments, a number of factors, including weather, time of day, rough terrain, high speeds, and hardware limitations, make performing vision-based simultaneous localization and mapping with current techniques infeasible due to factors such as image blur and/or underexposure, especially on smaller platforms and low-cost hardware. In this paper, we present novel visual place-recognition and odometry techniques that address the challenges posed by low lighting, perceptual change, and low-cost cameras. Our primary contribution is a novel two-step algorithm that combines fast low-resolution whole image matching with a higher-resolution patch-verification step, as well as image saliency methods that simultaneously improve performance and decrease computing time. The algorithms are demonstrated using consumer cameras mounted on a small vehicle in a mixed urban and vegetated environment and a car traversing highway and suburban streets, at different times of day and night and in various weather conditions. The algorithms achieve reliable mapping over the course of a day, both when incrementally incorporating new visual scenes from different times of day into an existing map, and when using a static map comprising visual scenes captured at only one point in time. Using the two-step place-recognition process, we demonstrate for the first time single-image, error-free place recognition at recall rates above 50% across a day-night dataset without prior training or utilization of image sequences. This place-recognition performance enables topologically correct mapping across day-night cycles
Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation
A Situationally Aware Voice-commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments

One long-standing challenge in robotics is the realization of mobile autonomous robots able to operate safely in human workplaces, and be accepted by the human occupants. We describe the development of a multiton robotic forklift intended to operate alongside people and vehicles, handling palletized materials within existing, active outdoor storage facilities. The system has four novel characteristics. The first is a multimodal interface that allows users to efficiently convey task-level commands to the robot using a combination of pen-based gestures and natural language speech. These tasks include the manipulation, transport, and placement of palletized cargo within dynamic, human-occupied warehouses. The second is the robot's ability to learn the visual identity of an object from a single user-provided example and use the learned model to reliably and persistently detect objects despite significant spatial and temporal excursions. The third is a reliance on local sensing that allows the robot to handle variable palletized cargo and navigate within dynamic, minimally prepared environments without a global positioning system. The fourth concerns the robot's operation in close proximity to people, including its human supervisor, pedestrians who may cross or block its path, moving vehicles, and forklift operators who may climb inside the robot and operate it manually. This is made possible by interaction mechanisms that facilitate safe, effective operation around people. This paper provides a comprehensive description of the system's architecture and implementation, indicating how real-world operational requirements motivated key design choices. We offer qualitative and quantitative analyses of the robot operating in real settings and discuss the lessons learned from our effort
An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robot's sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation, and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule.United States. Defense Advanced Research Projects Agency (United States. Air Force Research Laboratory Award FA8750-12-1-0321)United States. Office of Naval Research (Award N00014-12-1-0071