A Bank of Maximum &lt;italic&gt;A Posteriori&lt;/italic&gt; (MAP) Estimators for Target Tracking

Huang, Guoquan; Zhou, Ke; Trawny, Nikolas; Roumeliotis, Stergios I.

doi:10.1109/tro.2014.2378432

Cited by 19 publications

(32 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Distributed State Estimation (DSE) using mobile robot networks has a number of important applications, including robot localization [1], [2], SLAM [3]- [6], coverage [7], target localization [8]- [10] and tracking [11]- [16] among others. In these applications, the robots are equipped with sensing devices and collect information in order to minimize the uncertainty of the state.…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation Using Intermittently Connected Robot Networks

2019

View full text Add to dashboard Cite

This paper considers the problem of distributed state estimation using multi-robot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines (i) communication schedules that ensure that the network is intermittently connected, and (ii) samplingbased motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first distributed state estimation framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.Index Terms-Distributed state estimation, intermittent connectivity, sampling-based planning, multi-robot networks.

show abstract

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation Using Intermittently Connected Robot Networks

2019

View full text Add to dashboard Cite

show abstract

“…The target tracking problem, referred more generally as detection and tracking of moving objects (DTMO) 24 in the robotics literature, has been extensively studied for several decades. 25,26 The combined SLAM and DTMO problem, which is assessed in our work, has attracted considerable attention in the recent years, mostly in order to improve SLAM accuracy, which can be greatly degraded by the presence of dynamic objects in the environment, if the latter is considered as static. 27 The first mathematical framework to the combined process of simultaneous localization, mapping, and moving object tracking (SLAMMOT) was presented by Wang, 28 where the problem is decomposed into two separate estimators, one for the SLAM problem given the static landmarks and another for the tracking problem.…”

Section: Introductionmentioning

confidence: 99%

Vision-based dynamic target trajectory and ego-motion estimation using incremental light bundle adjustment

Chojnacki

Indelman

2018

International Journal of Micro Air Vehicles

View full text Add to dashboard Cite

This paper presents a vision-based, computationally efficient method for simultaneous robot motion estimation and dynamic target tracking while operating in GPS-denied unknown or uncertain environments. While numerous visionbased approaches are able to achieve simultaneous ego-motion estimation along with detection and tracking of moving objects, many of them require performing a bundle adjustment optimization, which involves the estimation of the 3D points observed in the process. One of the main concerns in robotics applications is the computational effort required to sustain extended operation. Considering applications for which the primary interest is highly accurate online navigation rather than mapping, the number of involved variables can be considerably reduced by avoiding the explicit 3D structure reconstruction and consequently save processing time. We take advantage of the light bundle adjustment method, which allows for ego-motion calculation without the need for 3D points online reconstruction, and thus, to significantly reduce computational time compared to bundle adjustment. The proposed method integrates the target tracking problem into the light bundle adjustment framework, yielding a simultaneous ego-motion estimation and tracking process, in which the target is the only explicitly online reconstructed 3D point. Our approach is compared to bundle adjustment with target tracking in terms of accuracy and computational complexity, using simulated aerial scenarios and real-imagery experiments.

show abstract

“…Applications in the area of distributed sensor planning and estimation that are directly relevant to our work include SLAM [14], localization [4]- [6], coverage [7], [8], mobile target tracking [1]- [3], [15], classification [16], and inverse problems for PDE-constrained systems [17], [18]. Compared to state estimation, inverse problems for PDE-constrained systems address both state and source identification and are particularly difficult to solve.…”

Section: Introductionmentioning

confidence: 99%

“…At the same time, comparing the possible posterior distributions can be computationally expensive, so these methods do not usually scale well with the number of targets and agents. Although many of these works assume that the robots are self localized with respect to a common global reference frame [1]- [4], [6], [15], [16], this is certainly not always the case; see, e.g., work on partially observable systems [23], [24] or the SLAM problem [14].…”

Section: Introductionmentioning

confidence: 99%

“…v s,k − z 2 ≤ p s,k − z 2 − 2α k (f (ž) − f (z)) + 1 4η p s,k −ž 2 + (1 + 4η)α 2 k L 2 f .Substituting this inequality in relation (50), we obtainz s,k − z 2 ≤ p s,k − z 2 − 2α k (f (ž) − f (z)) + 2β s,k δ s,k v s,k − z + 2β s,k |ν s,k | + 1 4η p s,k −ž 2 + A η,τ α 2 k L 2 f − τ − 1 τ (L h + D) 2 h 2 + (p s,k ; D s,k , ω s,k ),where A η,τ = 1+4η +τ . From relation(15), we have v s,k − z ≤ C z . Using this and the upper estimate for β s,k in(21)for bounding the three error terms completes the proof.Proof of Lemma V.8.…”

mentioning

confidence: 98%

See 1 more Smart Citation

Distributed Active State Estimation With User-Specified Accuracy

Freundlich

Lee

Zavlanos

2018

IEEE Trans. Automat. Contr.

View full text Add to dashboard Cite

Abstract-In this paper, we address the problem of controlling a network of mobile sensors so that a set of hidden states are estimated up to a user-specified accuracy. The sensors take measurements and fuse them online using an Information Consensus Filter (ICF). At the same time, the local estimates guide the sensors to their next best configuration. This leads to an LMI-constrained optimization problem that we solve by means of a new distributed random approximate projections method. The new method is robust to the state disagreement errors that exist among the robots as the ICF fuses the collected measurements. Assuming that the noise corrupting the measurements is zeromean and Gaussian and that the robots are self localized in the environment, the integrated system converges to the next best positions from where new observations will be taken. This process is repeated with the robots taking a sequence of observations until the hidden states are estimated up to the desired user-specified accuracy. We present simulations of sparse landmark localization, where the robotic team achieves the desired estimation tolerances while exhibiting interesting emergent behavior.

show abstract

A Bank of Maximum <italic>A Posteriori</italic> (MAP) Estimators for Target Tracking

Cited by 19 publications

References 31 publications

Distributed State Estimation Using Intermittently Connected Robot Networks

Distributed State Estimation Using Intermittently Connected Robot Networks

Vision-based dynamic target trajectory and ego-motion estimation using incremental light bundle adjustment

Distributed Active State Estimation With User-Specified Accuracy

Contact Info

Product

Resources

About