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Collect useful sentences in Papers (14)

2018-03-19T08:36:00.000-07:00

Cooperative localization has been an active area
of research (e.g., [2–9]) because it provides several potential advantages, including increased localization accuracy, sensor coverage, robustness,
efficiency, and flexibility.

These algorithms can be used either
in centralized [6] or decentralized manner [3, 10,13].

For the localization errors to be bounded, it is required that the system be observable,
independent of the estimation technique being
used.

linear
approximation of a nonlinear system can provide
different structural properties regarding the observability.

They compared the observability
properties of range and bearing measurements
and showed that with either type of measurement,
the maximum rank of the observability matrix is
three, i.e., not fully observable.

In our previous work [1], we have extended

Reference:

Sharma, R., Quebe, S., Beard, R. W., & Taylor, C. N. (2013). Bearing-only cooperative localization. Journal of Intelligent & Robotic Systems, 72(3-4), 429-440.

Collect useful sentences in Papers (13)

2018-03-07T20:06:00.000-08:00

There have been numerous approaches to the localization problem utilizing different types of sensors [2] and a variety of techniques (e.g.,
[3]–[7]).

The key idea behind most of the current localization
schemes is to optimally combine measurements from proprioceptive sensors that monitor the motion of the vehicle with information collected by exteroceptive sensors that provide a representation of the environment and its signals.

The various approaches to localization differ primarily in the type of estimator
invoked in order to filter out the measurement noise and reduce
the uncertainty associated with the interpretation of the sensor
signals.

Even when a group of, say , robots is considered, the group localization problem is usually resolved by
independently solving pose estimation problems.

On the other hand, a more coordinated schema for localization has a number of advantages that can compensate for
the added complexity

measure a characteristic property of
the area, such as the local vector of the earth’s magnetic field

Properly combining all
this information will result in a single estimate of increased accuracy and reduced uncertainty.

a better estimate
of the position and orientation of a landmark can, in turn, drastically improve the outcome of the robot localization process and
thus, this group will benefit from this collaboration schema.

The advantages stemming from the exchange of information
among the members of a group are more crucial in the case
of heterogeneous robotic colonies.

When a team of robots is
composed of different platforms carrying different proprioceptive and exteroceptive sensors and thus, having different capabilities for self-localization, the quality of the localization estimates will vary significantly across the individual members.

The authors acknowledge that

The authors deal with the problem of exploration of an unknown environment using two mobile robots.

Although practices like those previously mentioned can be
supported within our framework (Sections VI and VII), the advantage of the CL approach is that it provides a solution to
the most general case, in which all the robots in the group can
move simultaneously while continuous visual contact or a map
of the area is not required.

In order to treat the group localization problem, we begin from the reasonable assumptions

The main drawback of addressing the cooperative localization problem as an information combination problem within a single entity (“group organism”) is that it requires centralized
processing and communication.

The CL approach
accomplishes precisely this goal. Since the sensing modalities of
the group are distributed, so too should be the processing modules.

Reference:

Roumeliotis, S. I., & Bekey, G. A. (2002). Distributed multirobot localization. IEEE Transactions on Robotics and Automation, 18(5), 781-795.

Collect useful sentences in Papers (12)

2018-03-07T08:54:00.000-08:00

Second, we assume that each robot is
equipped with a robot sensor that allows it to measure the
relative pose and identity of nearby robots; robot sensors
can he readily constructed using cameras and/or scanning
laser range finders in combination with coded fiducials
placed on the robots.

The chief drawback of the approach is its computational
cost: particle filters are relatively expensive,

Recently,
some authors have also considered the related problem
of map building with multiple robots [14]. All of these
authors make use of statistical or probabilistic techniques;
the common tools of choice are Kalman filters, particle
filters, maximum likelihood estimation and expectation
maximization.

While our approach does not require such explicit cooperation on the part of robots, the accuracy of localization can
certainly be improved by the adoption of such strategies.

Reference:

Howard, A., Mataric, M. J., & Sukhatme, G. S. (2003, September). Putting the'I'in'team': An ego-centric approach to cooperative localization. In Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on (Vol. 1, pp. 868-874). IEEE.

Collect useful sentences in Papers (11)

2018-03-05T09:02:00.001-08:00

To solve this problem, some AUVs opt to surface
often and use a GPS to correct the position estimate.

Other
AUVs employ fixed beacons in the form of Long Baseline
(LBL) or Ultra Short Baseline (USBL) acoustic systems to
estimate their positions underwater [4] at the cost of substantial
support infrastructure cost and deployment effort

In many
cases, the problem has only been considered in the context of
fixed beacons, while in other cases (see [12]) the beacon is
assumed to some arbitrary zig-zag pattern.

Reference:

Rui, G., & Chitre, M. (2010, May). Cooperative positioning using range-only measurements between two AUVs. In OCEANS 2010 IEEE-Sydney (pp. 1-6). IEEE.

The ability of a robot localize itself is a fundamental
problem for mobile robots. Not surprisingly, many technologies and techniques for robot localization can be found
in the literature (eg. [l, 8, 9, 141)

Reference:

Kantor, G., & Singh, S. (2002). Preliminary results in range-only localization and mapping. In Robotics and Automation, 2002. Proceedings. ICRA'02. IEEE International Conference on (Vol. 2, pp. 1818-1823). Ieee.

Collect useful sentences in Papers (10)

2018-03-05T07:58:00.001-08:00

Dead reckoning based on these
sensors suffers from a problem of unbounded navigation error
growth over time.

The idea of cooperative localization with a few vehicles
that know their position well and other AUVs with poor
navigational sensors is not new.

Cooperative AUVs typically need to communicate in order
to cooperate.

Although all of these authors acknowledge that the
relative motion of the beacon vehicle and the survey AUVs is
key to having single beacon range-only navigation perform
well, the problem of determining the optimal path of the
beacon vehicle given the desired path of the survey AUVs has
received little attention

Although [8] assumes that the path of the beacon
vehicle is produced in such a way that it improves the position
estimate of the survey AUV, we do not present the algorithm
to do so in that paper.

The work presented
here is complementary and may be used with many of the
range-only position estimation algorithms previously proposed
by other researchers (e.g. [4], [8])

Reference:

Chitre, M. (2010, June). Path planning for cooperative underwater range-only navigation using a single beacon. In Autonomous and Intelligent Systems (AIS), 2010 International Conference on (pp. 1-6). IEEE.

Collect useful sentences in Papers (9)

2018-03-05T07:26:00.000-08:00

Independent of the quality of the sensors used, the error in the
position estimate based on dead-reckoning information grows
without bound

Errors as low as 0.1% can be obtained with large and
expensive INS systems, but for vehicles relying only on a compass and a speed estimate, errors can be as high as 20%.

By
surfacing the AUV can obtain a position update through its
GPS, but this is impossible (under ice) or undesirable for many
applications.

The motivation behind our research is to enable multiple
AUVs to cooperatively navigate.

The concept of portable landmarks as outlined in Kurazume et al. (1994) is not feasible as it is often
difficult for an AUV to hold its position, especially in strong
currents

The objective for our work is to develop and test an algorithm for cooperative positioning of multiple mobile undersea
vehicles that can use acoustic modems concurrently for both
ranging and for communication (Freitag et al. 2001)

Reference:

Bahr, A., Leonard, J. J., & Fallon, M. F. (2009). Cooperative localization for autonomous underwater vehicles. The International Journal of Robotics Research, 28(6), 714-728.

Collect useful sentences in Papers (8)

2018-03-03T11:24:00.001-08:00

In the last years cooperative robotics has received considerable attention.

Teams of robots are able to overcome
the limitations of single robots and allow to attack more
difficult tasks.

New challenges appear providing new research
areas.

As compared to vision-based
SLAM ([3], [18]), stereo matching and reconstruction ([4]),
ground-plane based navigation, or other depth reconstruction techniques, extracting bearings is a simple process,
close to obtaining raw data.

In order to take into account heterogeneous pairs of robots and possible hardware
failures we have implemented our localization algorithm
for two different configurations.

Many
of the proposed tracking methods do not provide pose
estimation, due to the fact that feature models do not
contain structure information.

cooperative localization has been
an active research area during the last years in the
robotic community.

There exist different approaches depending on several aspects as the information used
(map availability, static/dynamic features) or the multirobot architecture (communication capabilities, centralized/decentralized).

In
the absence of static references these methods estimate the
relative pose among the robots instead of their location on a global reference system. However, all these algorithms use
range-bearing sensors that provide an accurate estimation
of their relative positions.

Our method, instead of restricting
the motion of the robots with a known model, uses the
measurements of their displacements to turn the problem
observable.

Reference:

Montesano, L., Gaspar, J., Santos-Victor, J., & Montano, L. (2005, August). Cooperative localization by fusing vision-based bearing measurements and motion. In Intelligent Robots and Systems, 2005.(IROS 2005). 2005 IEEE/RSJ International Conference on (pp. 2333-2338). IEEE.

Collect useful sentences in Papers (7)

2018-03-03T10:28:00.000-08:00

These solutions are however unfeasible or
unreliable in many real-world situations, for example when
the robots are operating in unstructured indoor environments or under the surface of the water. A much preferable
approach is to preserve the system autonomy by letting
the robots perform the localization on their own.

In this
case, robot-to-robot measurements are collected and broadcast to the group to be used in a cooperative localization
scheme.

Clearly, this approach requires that each robot is
equipped with a sensor that provides some form of relative measurement (e.g. relative distance or bearing) as well
as a communication module for transmitting and receiving
information.

All of these
works show that the agents’ ability of sensing each other
can improve the localization of the entire system.

To the best of the authors’ knowledge, in all previous
works on network, cooperative, or mutual localization it is
assumed that relative measurements include the identity of
the robots, i.e. that the sensor ‘knows’ to which robot each
measurement refers to. This ubiquitous assumption requires
in practice the identification of a distinctive feature for each
robot, an ability that is not embedded in simple sensors
(e.g. cameras, range finders, RGB-D scanners) and typically calls for sophisticated classification algorithms based
on some form of robot tagging.

the process may be prone
to failures and false positives whenever the conditions of
the surrounding environment are not ideal (e.g. visual tagging may be dramatically affected by slight changes in the
lighting conditions).

Second, it is difficult to discriminate
robots in large groups using features such as shape or color,
i.e. most kinds of tagging do not scale well with the number
of robots.

In this paper we develop to a mature stage the latter approach.

Reference:

Franchi, A., Oriolo, G., & Stegagno, P. (2013). Mutual localization in multi-robot systems using anonymous relative measurements. The International Journal of Robotics Research, 32(11), 1302-1322.

Statements about the sentences in papers

2018-03-02T10:57:00.003-08:00

The series "collect useful sentences in papers" published in my Blog are all sentences written in each good paper. Most of those sentences are strong statements used in introduction section. Part of those sentences are good organization sentences.

What I did is to copy sentences from the original paper and put in posts. In other word, I did not modify any words in those sentences. Also, I put the reference in each post, which means all sentences in one post are from this reference. If the author want to keep it unpublished in my Blog, please comment in the post, so I will save that post as private.

Collect useful sentences in Papers (6)

2018-02-28T07:54:00.000-08:00

the positioning solutions from these types of navigational aids are only available when the receiver has uninterrupted access to at least four satellites (GNSS)

While overall satellite coverage of the Earth has increased, environments such as urban canyons and inside buildings can prevent the acquisition of the required satellite signals.

At the same time, there is an increasing desire to develop autonomous, miniature vehicles

The use of magnetic field variations to aid outdoor navigation has been suggested by various authors.

This is an important tool as vehicles become smaller because heading measurements generally come from gyroscopes, and accurate gyroscopes are generally bulky.

By doing this, Wilson et al. do not take advantage of all the information available from the magnetic field.

Reference:

Storms, W., Shockley, J., & Raquet, J. (2010, October). Magnetic field navigation in an indoor environment. In Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2010 (pp. 1-10). IEEE.

Collect useful sentences in Papers (5)

2018-02-27T09:14:00.001-08:00

Alternative technologies are sought for such difficult environments

Indoor positioning technologies can be classified into three categories: technologies based on signals of opportunity, technologies based on pre-deployed infrastucture, and others.

The signals-of-opportunity are signals (radio frequency - RF - signals are the most common) that are not intended for positioning and navigation, such as signals of WiFi, television and FM radio. Since these signals are designed for other purposes, and given the reality of the harsh signal propagation environment, using them for positioning to achieve high accuracy is a very difficult, if not impossible, task [1, 2].

The low cost and wide coverage of such methods are the main advantages. "fingerprinting"

There are many positioning technologies that require the deployment of infrastructure, such as positioning systems using infrared, ultrasound and ultra wide band [3-5], as well as bespoke RF-based systems.

Deploying new infrastructure is costly, and hence the coverage is often very limited - typically using "hot spot" mode.

A significant advantage of this type of system is that the artificially generated magnetic field is not affected by most obstacles, hence multipath or non-line-of-sight errors are avoided.

Using the geomagnetic field for outdoor navigation is not new.

An obvious advantage of using the magnetic field for positioning is that no infrastructure needs to be pre-deployed, which makes such a system cost effective.

many animals use the geomagnetic field naturally to navigate their way around the Earth [18]

Reference:

Li, B., Gallagher, T., Dempster, A. G., & Rizos, C. (2012, November). How feasible is the use of magnetic field alone for indoor positioning?. In Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on (pp. 1-9). IEEE.

Collect useful sentences in Papers (4)

2018-02-26T09:47:00.002-08:00

At first glance, one could solve the problem of localizing N robots by localizing each robot independently, which is a valid approach that might yield reasonable results in many environments.

When a robot determines the location of another robot relative to its own, both robots can refine their internal beliefs based on the other robot’s estimate, hence improve their localization accuracy

The ability to exchange information during localization is particularly attractive in the context of global localization, where each sight of another robot can reduce the uncertainty in the estimated location dramatically

By transferring information across multiple robots, sensor information can be leveraged.

Consequently, phrasing the problem of localization as a collaborative one offers the opportunity of improved performance from less data.

Reference:

Fox, D., Burgard, W., Kruppa, H., & Thrun, S. (2000). A probabilistic approach to collaborative multi-robot localization. Autonomous robots, 8(3), 325-344.

Collect useful sentences in Papers (3)

2018-02-22T09:40:00.001-08:00

Many researchers have proposed the use of micro air vehicles (MAVs) as a promising alternative to ground robot platforms for rescue tasks and a host of other applications.

MAVs are already being used in several military and civilian domains, including surveillance operations, weather observation, disaster relief coordination, and civil engineering inspections.

As a result, previous autonomous MAVs have been limited in their ability to operate in these areas.

We describe experimental assessments of first using onboard sensors to estimate the vehicle’s position and second using the same sensor data to build a map of the environment around the vehicle, a process generally called simultaneous localization and mapping (SLAM).

due to a combination of limited payloads for sensing and computation, coupled with the fast dynamics of air vehicles, the algorithms have generally been tested in simulation, on ground robots, or with sensor data first collected from a manually piloted MAV

leverages existing algorithms to balance the trade-offs imposed by GPS-denied flight.

In the ground robotics domain, many algorithms exist for accurate localization in large-scale environments; however, these algorithms are usually deployed on slow-moving robots that cannot handle even moderately rough terrain.

Limited payload reduces the computational power available onboard and eliminates popular sensors such as SICK laser scanners, large-aperture cameras, and high-fidelity inertial measurement units (IMUs)

Indirect position estimates Whereas MAVs will generally have an IMU, double-integrating acceleration measurements from lightweight MEMS IMUs results in prohibitively large position errors.

Fast dynamics MAVs have fast and unstable dynamics that result in a host of sensing, estimation, control, and planning implications for the vehicle.

Constant motion Unlike ground vehicles, a MAV cannot simply stop and perform more sensing or computation when its state estimates have large uncertainties. Instead, the vehicle is likely to be unable to estimate its position and velocity accurately, and as a result, it may pick up speed or oscillate, degrading the sensor measurements further.

In recent years, the development of autonomous flying robots has been an area of increasing research interest.

This research has produced a number of systems with a wide range of capabilities when operating in outdoor environments.

Although these are all challenging research areas in their own right, and pieces of the previous work (such as the modeling and control techniques) carry over to the development of vehicles operating without GPS

Whereas outdoor vehicles can usually rely on GPS, there are many situation in which relying on GPS would be unsafe, because the GPS signal can be lost due to multipath, satellites being occluded by buildings and foliage, or even intentional jamming.

In response to these concerns, a number of researchers have developed systems that rely on vision for control of the vehicle.

Although the systems developed by these researchers share many of the challenges faced by indoor or urban MAVs, they operate on vehicles that are orders of magnitude larger, with much greater sensing and computation payloads.

In addition, the outdoor environments tend to be much less cluttered, which gives greater leeway for errors in the state estimation and control

Although their MAVs are able to hover autonomously, they do not achieve any sort of autonomous goal-directed flight that would enable the systems to be controlled at a high level such that they could be built upon for more advanced autonomous applications

To enable tractable vision processing, this work has typically made strong (and brittle) assumptions about the environment.

Their applicability is therefore constrained to environments with specific features and does not allow for general navigation in GPS-denied environments.

However, none of these papers presented experimental results demonstrating the ability to stabilize all six degrees of freedom of the MAV using the onboard sensors, and all made use of prior maps, an assumption that is relaxed in this work.

The system presented in this paper was also discussed in Bachrach, He, and Roy (2009a, 2009b)

Here we present a more detailed analysis and evaluation of the algorithms, as well as significantly expanded experimental results in the field, including new results in large-scale indoor environments and outdoor flight in the urban canyon.

Reference:

Bachrach, A., Prentice, S., He, R., & Roy, N. (2011). RANGE–Robust autonomous navigation in GPS‐denied environments. Journal of Field Robotics, 28(5), 644-666.

Collect useful sentences in Papers (2)

2018-02-21T13:02:00.005-08:00

Outdoor ground vehicle navigation was explored by Shockley [3]

These sources still corrupt measurements, but to a much lesser extent

The act of increasing altitude essentially acts as a low pass filter on Earth’s magnetic field. This loss of frequency information degrades navigation accuracy, which is why at satellite altitudes navigation accuracy using magnetic fields is so low.

magnetic anomaly maps, which capture the highest frequency components of Earth’s magnetic field, are widely available around the globe.

Terrain systems can only operate over the one-third of Earth covered by land. High-frequency spatial features in the magnetic anomaly field exist over both land and water. This is also true for desert-like environments with very little terrain height variation – magnetic anomaly maps still contain the needed high-spatial frequency content needed to navigate

Alternative systems such as vision-aided navigation systems often rely on daylight and weather conditions to operate.

Another benefit of magnetic anomaly navigation is the fact that the instrumentation for making magnetic measurements is mature.

OPC magnetometers are extremely accurate absolute instruments. Gravity gradiometry systems are limited by the relatively poor accuracy of existing sensors [13]

jamming a magnetic anomaly navigation system would be more difficult than jamming radio frequency (RF)-based navigation systems.

Magnetic anomaly maps are often accurately modeled as a a grid of magnetic dipoles. When moving away from an RF source, signals decrease in magnitude at a rate of r^2. When moving away from a magnetic dipole, the magnitude of the signal decreases as a rate of r^5 [14].

Reference:

Canciani, A., & Raquet, J. (2016). Absolute positioning using the Earth's magnetic anomaly field. Navigation, 63(2), 111-126.

Collect useful sentences in Papers (1)

2018-02-13T12:52:00.003-08:00

To enable closed-loop feedback control for autonomous operation, UAVs need an accurate estimate of their position and attitude with respect to the surrounding environment.

The GPS position update is critical to the functioning of such an architecture.

However, these sensors typically rely on the emission and reception of a signal to determine range which is sometimes undesirable if the vehicle needs to remain undetected.

However, these sensors typically rely on the emission and reception of a signal to determine range which is sometimes undesirable if the vehicle needs to remain undetected.

a camera is a standard payload on any surveillance and reconnaissance UAV

An integrated vision-aided INS (V-INS) navigation system is passive, and hence not easily prone to jamming or detection

Aerial vehicles have fast dynamics, are resource constrained, and rotorcraft/VTOL UAVs are unstable in position and/or attitude. This coupling makes it desirable to incorporate inertial measurements from the IMU rather than relying on purely camera-based monocular SLAM

UAVs typically have strict payload and power constraints that limit the computational power available onboard, limiting the class of vision-based navigation techniques that can be practically applied

While their goals are similar to those presented in this paper,

Furthermore, the flight-test results of the stereo system, presented by Haines (2011), do not demonstrate the extended flight distances and durations demonstrated in our paper

The key missing element was

Weiss et al. (2011) described an impressive monocular slam implementation and LQR controller

Hence, while the results in this work are impressive, this approach does not conclusively provide a comprehensive solution to the vision-aided navigation and control problem

There is an ongoing debate in the SLAM community about the superiority of EKF-SLAM versus PTAM and keyframe-based algorithms.

Attempts have been made to resolve which approach is better

verdict

Leading techniques for vision-aided navigation was not used in GPS-denied closed-loop waypoint-following flight and/or GPS-denied landing

they were validated through simulation only

most results were implemented on ground vehicles, which have slower and stable dynamics and any closed-loop flight tests were conducted in controlled environments

Reference:

Chowdhary, G., Johnson, E. N., Magree, D., Wu, A., & Shein, A. (2013). GPS‐denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft. Journal of Field Robotics, 30(3), 415-438.