Moving-baseline localization for mobile wireless sensor networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (leaves 93-98).The moving-baseline localization (MBL) problem arises when a group of nodes moves through an environment in which no external coordinate reference is available. When group members cannot see or hear one another directly, each node must employ local sensing and inter-device communication to infer the spatial relationship and motion of all other nodes with respect to itself. We consider a setting in which nodes move with piecewise-linear velocities in the plane, and any node can exchange noisy range estimates with certain sufficiently nearby nodes. We develop a distributed solution to the MBL problem in the plane, in which each node performs robust hyperbola fitting, trilateration with velocity constraints, and subgraph alignment to arrive at a globally consistent view of the network expressed in its own "rest frame." Changes in any node's motion cause deviations between observed and predicted ranges at nearby nodes, triggering revision of the trajectory estimates computed by all nodes. We implement and analyze our algorithm in a simulation informed by the characteristics of a commercially available ultra-wideband (UWB) radio, and show that recovering node trajectories, rather than just locations, requires substantially less computation at each node. Finally, we quantify the minimum ranging rate and local network density required for the method's successful operation.by Jun-geun Park.S.M

Range-only benthic Rover localization off the central California coast

Nowadays, the use of autonomous vehicles for ocean research has increased, since these vehicles have a better cost/performance ratio than crewed vessels or oceanographic ships. For example, autonomous surface vehicles can be used to localize underwater targets. This paper describes a mission to find a crawling robot - Benthic Rover - on the abyssal plain in the north eastern Pacific, using single-beacon localization from onboard a Wave Glider autonomous surface vehicle. While the Wave Glider is moving around the surface in the target zone, it takes ranges between the target and itself using acoustic modems. With these ranges it can compute the target location, as a Long Baseline (LBL) system. The benefit of this approach is the reduction of cost and complexity relative to deployment of a traditional shipboard LBL system. Additionally, this is a mobile system, and can cover long distances, and can geolocate multiple targets over a large area.Postprint (author's final draft

Spatio-temporal Video Re-localization by Warp LSTM

The need for efficiently finding the video content a user wants is increasing because of the erupting of user-generated videos on the Web. Existing keyword-based or content-based video retrieval methods usually determine what occurs in a video but not when and where. In this paper, we make an answer to the question of when and where by formulating a new task, namely spatio-temporal video re-localization. Specifically, given a query video and a reference video, spatio-temporal video re-localization aims to localize tubelets in the reference video such that the tubelets semantically correspond to the query. To accurately localize the desired tubelets in the reference video, we propose a novel warp LSTM network, which propagates the spatio-temporal information for a long period and thereby captures the corresponding long-term dependencies. Another issue for spatio-temporal video re-localization is the lack of properly labeled video datasets. Therefore, we reorganize the videos in the AVA dataset to form a new dataset for spatio-temporal video re-localization research. Extensive experimental results show that the proposed model achieves superior performances over the designed baselines on the spatio-temporal video re-localization task

Localization under consistent assumptions over dynamics

Accurate maps are a prerequisite for virtually all autonomous vehicle tasks. Most state-of-the-art maps assume a static world, and therefore dynamic objects are filtered out of the measurements. However, this division ignores movable but non-moving, i.e. semi-static, objects, which are usually recorded in the map and treated as static objects, violating the static world assumption, causing error in the localization. In this paper, we present a method for modeling moving and movable objects for matching the map and the measurements consistently. This reduces the error resulting from inconsistent categorization and treatment of non-static measurements. A semantic segmentation network is used to categorize the measurements into static and semi-static classes, and a background subtraction-based filtering method is used to remove dynamic measurements. Experimental comparison against a state-of-the-art baseline solution using real-world data from Oxford Radar RobotCar data set shows that consistent assumptions over dynamics increase localization accuracy.Comment: Submitted to IEEE-ICRA-202

Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images.

BackgroundLiver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration.MethodsThree hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models.ResultsCompared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ≤ 0.020).ConclusionA fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration

Spatial consequences of bridging the saccadic gap

We report six experiments suggesting that conscious perception is actively redrafted to take account of events both before and after the event that is reported. When observers saccade to a stationary object they overestimate its duration, as if the brain were filling in the saccadic gap with the post-saccadic image. We first demonstrate that this illusion holds for moving objects, implying that the perception of time, velocity, and distance traveled become discrepant. We then show that this discrepancy is partially resolved up to 500 ms after a saccade: the perceived offset position of a post-saccadic moving stimulus shows a greater forward mislocalization when pursued after a saccade than during pursuit alone. These data are consistent with the idea that the temporal bias is resolved by the subsequent spatial adjustment to provide a percept that is coherent in its gist but inconsistent in its detail