Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments

Existing simultaneous localization and mapping (SLAM) algorithms are not robust in challenging low-texture environments because there are only few salient features. The resulting sparse or semi-dense map also conveys little information for motion planning. Though some work utilize plane or scene layout for dense map regularization, they require decent state estimation from other sources. In this paper, we propose real-time monocular plane SLAM to demonstrate that scene understanding could improve both state estimation and dense mapping especially in low-texture environments. The plane measurements come from a pop-up 3D plane model applied to each single image. We also combine planes with point based SLAM to improve robustness. On a public TUM dataset, our algorithm generates a dense semantic 3D model with pixel depth error of 6.2 cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS) 201

Towards consistent visual-inertial navigation

Visual-inertial navigation systems (VINS) have prevailed in various applications, in part because of the complementary sensing capabilities and decreasing costs as well as sizes. While many of the current VINS algorithms undergo inconsistent estimation, in this paper we introduce a new extended Kalman filter (EKF)-based approach towards consistent estimates. To this end, we impose both state-transition and obervability constraints in computing EKF Jacobians so that the resulting linearized system can best approximate the underlying nonlinear system. Specifically, we enforce the propagation Jacobian to obey the semigroup property, thus being an appropriate state-transition matrix. This is achieved by parametrizing the orientation error state in the global, instead of local, frame of reference, and then evaluating the Jacobian at the propagated, instead of the updated, state estimates. Moreover, the EKF linearized system ensures correct observability by projecting the most-accurate measurement Jacobian onto the observable subspace so that no spurious information is gained. The proposed algorithm is validated by both Monte-Carlo simulation and real-world experimental tests.United States. Office of Naval Research (N00014-12-1- 0093, N00014-10-1-0936, N00014-11-1-0688, and N00014-13-1-0588)National Science Foundation (U.S.) (Grant IIS-1318392

RISE: An Incremental Trust-Region Method for Robust Online Sparse Least-Squares Estimation

Many point estimation problems in robotics, computer vision, and machine learning can be formulated as instances of the general problem of minimizing a sparse nonlinear sum-of-squares objective function. For inference problems of this type, each input datum gives rise to a summand in the objective function, and therefore performing online inference corresponds to solving a sequence of sparse nonlinear least-squares minimization problems in which additional summands are added to the objective function over time. In this paper, we present Robust Incremental least-Squares Estimation (RISE), an incrementalized version of the Powell's Dog-Leg numerical optimization method suitable for use in online sequential sparse least-squares minimization. As a trust-region method, RISE is naturally robust to objective function nonlinearity and numerical ill-conditioning and is provably globally convergent for a broad class of inferential cost functions (twice-continuously differentiable functions with bounded sublevel sets). Consequently, RISE maintains the speed of current state-of-the-art online sparse least-squares methods while providing superior reliability.United States. Office of Naval Research (Grant N00014-12-1-0093)United States. Office of Naval Research (Grant N00014-11-1-0688)United States. Office of Naval Research (Grant N00014-06-1-0043)United States. Office of Naval Research (Grant N00014-10-1-0936)United States. Air Force Research Laboratory (Contract FA8650-11-C-7137

Autonomic nervous system and hypothalamic–pituitary–adrenal axis response to experimentally induced cold pain in adolescent non-suicidal self-injury – study protocol

Background: Adolescent non-suicidal self-injury (NSSI) is associated with altered sensitivity to experimentally induced pain. Adolescents engaging in NSSI report greater pain threshold and pain tolerance, as well as lower pain intensity and pain unpleasantness compared to healthy controls. The experience of pain is associated with reactivity of both the autonomic nervous system (ANS) and the hypothalamic–pituitary–adrenal (HPA) axis. However, previous research has not yet systematically addressed differences in the physiological response to experimentally induced pain comparing adolescents with NSSI and age- and sex-matched healthy controls. Methods/Design: Adolescents with NSSI and healthy controls undergo repeated painful stimulation with the cold pressor task. ANS activity is continuously recorded throughout the procedure to assess changes in heart rate and heart rate variability. Blood pressure is monitored and saliva is collected prior to and after nociceptive stimulation to assess levels of saliva cortisol. Discussion: The study will provide evidence whether lower pain sensitivity in adolescents with NSSI is associated with blunted physiological and endocrinological responses to experimentally induced pain compared to healthy controls. Extending on the existing evidence on altered pain sensitivity in NSSI, measured by self-reports and behavioural assessments, this is the first study to take a systematic approach in evaluating the physiological response to experimentally induced pain in adolescent NSSI. Trial Registration: Deutsche Register Klinischer Studien, Study ID: DRKS00007807 ; Trial Registration Date: 13.02.201

The Bayes Tree: Enabling Incremental Reordering and Fluid Relinearization for Online Mapping

In this paper we present a novel data structure, the Bayes tree, which exploits the connections between graphical model inference and sparse linear algebra. The proposed data structure provides a new perspective on an entire class of simultaneous localization and mapping (SLAM) algorithms. Similar to a junction tree, a Bayes tree encodes a factored probability density, but unlike the junction tree it is directed and maps more naturally to the square root information matrix of the SLAM problem. This makes it eminently suited to encode the sparse nature of the problem, especially in a smoothing and mapping (SAM) context. The inherent sparsity of SAM has already been exploited in the literature to produce efficient solutions in both batch and online mapping. The graphical model perspective allows us to develop a novel incremental algorithm that seamlessly incorporates reordering and relinearization. This obviates the need for expensive periodic batch operations from previous approaches, which negatively affect the performance and detract from the intended online nature of the algorithm. The new method is evaluated using simulated and real-world datasets in both landmark and pose SLAM settings

Health related quality of life and psychopathological distress in risk taking and self-harming adolescents with full-syndrome, subthreshold and without borderline personality disorder: rethinking the clinical cut-off?

Background: Diagnostic standards do not acknowledge developmental specifics and differences in the clinical presentation of adolescents with borderline personality disorder (BPD). BPD is associated with severe impairments in health related quality of life (HRQoL) and increased psychopathological distress. Previously no study addressed differences in HRQoL and psychopathology in adolescents with subthreshold and full-syndrome BPD as well as adolescents at-risk for the development but no current BPD. Methods: Drawing on data from a consecutive sample of N = 264 adolescents (12–17 years) presenting with risk-taking and self-harming behavior at a specialized outpatient clinic, we investigated differences in HRQoL (KIDSCREEN-52) and psychopathological distress (SCL-90-R) comparing adolescents with no BPD (less than 3 criteria fulfilled), to those with subthreshold (3–4 BPD criteria) and full-syndrome BPD (5 or more BPD criteria). Group differences were analyzed using one-way analysis of variance with Sidak corrected contrasts or Chi-Square test for categorical variables. Results: Adolescents with subthreshold and full-syndrome BPD presented one year later at our clinic and were more likely female. Adolescents with subthreshold and full-syndrome BPD showed greater Axis-I and Axis-II comorbidity compared to adolescents with no BPD, and reported greater risk-taking behaviour, self-injury and suicidality. Compared to those without BPD, adolescents with subthreshold and full-syndrome BPD reported significantly reduced HRQoL. Adolescents with sub-threshold BPD and those with full-syndrome BPD did not differ on any HRQoL dimension, with the exception of Self-Perception. Similar, groups with sub-threshold and full-syndrome BPD showed no significant differences on any dimension of self-reported psychopathological distress, with the exception of Hostility. Conclusions: Findings highlight that subthreshold BPD in adolescents is associated with impairments in HRQoL and psychopathological distress comparable to full-syndrome BPD. Findings raise awareness on the importance of early detection and question the diagnostic validity and clinical utility of existing cut-offs. Findings support a lower diagnostic cut-off for adolescent BPD, to identify those at-risk at an early stage