Kalman Filter Tuning with Bayesian Optimization

Many state estimation algorithms must be tuned given the state space process and observation models, the process and observation noise parameters must be chosen. Conventional tuning approaches rely on heuristic hand-tuning or gradient-based optimization techniques to minimize a performance cost function. However, the relationship between tuned noise values and estimator performance is highly nonlinear and stochastic. Therefore, the tuning solutions can easily get trapped in local minima, which can lead to poor choices of noise parameters and suboptimal estimator performance. This paper describes how Bayesian Optimization (BO) can overcome these issues. BO poses optimization as a Bayesian search problem for a stochastic ``black box'' cost function, where the goal is to search the solution space to maximize the probability of improving the current best solution. As such, BO offers a principled approach to optimization-based estimator tuning in the presence of local minima and performance stochasticity. While extended Kalman filters (EKFs) are the main focus of this work, BO can be similarly used to tune other related state space filters. The method presented here uses performance metrics derived from normalized innovation squared (NIS) filter residuals obtained via sensor data, which renders knowledge of ground-truth states unnecessary. The robustness, accuracy, and reliability of BO-based tuning is illustrated on practical nonlinear state estimation problems,losed-loop aero-robotic control

Time Dependence in Kalman Filter Tuning

In this paper, we propose an approach to address the problems with ambiguity in tuning the process and observation noises for a discrete-time linear Kalman filter. Conventional approaches to tuning (e.g. using normalized estimation error squared and covariance minimization) compute empirical measures of filter performance. The parameters are selected, either manually or by some kind of optimization algorithm, to maximize these measures of performance. However, there are two challenges with this approach. First, in theory, many of these measures do not guarantee a unique solution due to observability issues. Second, in practice, empirically computed statistical quantities can be very noisy due to a finite number of samples. We propose a method to overcome these limitations. Our method has two main parts to it. The first is to ensure that the tuning problem has a single unique solution. We achieve this by simultaneously tuning the filter over multiple different prediction intervals. Although this yields a unique solution, practical issues (such as sampling noise) mean that it cannot be directly applied. Therefore, we use Bayesian Optimization. This technique handles noisy data and the local minima that it introduces. We demonstrate our results in a reference example and demonstrate that we are able to obtain good results. We share the source code for the benefit of the community1

Autonomous Flight in Unknown Indoor Environments

http://multi-science.metapress.com/content/80586kml376k2711/This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner. Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.National Science Foundation (U.S.) (NSF Division of Information and Intelligent Systems under grant # 0546467)United States. Army Research Office (ARO MAST CTA)Singapore. Armed Force

Dry Matter Accumulation and Partitioning between Vegetative and Reproductive Organs in Alfalfa (\u3ci\u3eMedicago sativa\u3c/i\u3e L.)

This work investigated the partitioning of dry matter between vegetative and reproductive plant organs in alfalfa during the reproductive period under field conditions. Two French varieties (Europe and Magali) were studied. Both varieties showed similar growth pattern of the different plant organs in 1998 and 1999. The mean dry matter of vegetative organs (shoots and leaves) over the two years was higher in Europe (567g/m2) than Magali (470g/m2). No vegetative growth was observed during the reproductive period. The root organs (measured to a depth of 0.20 m) and the reproductive organs showed an increase in dry matter accumulation during the first 300 °Cd and 600 °Cd, respectively. It indicated that dry matter was preferentially partitioned to the reproductive organs during the first 600 °Cd. The root organs seem to be a competing sink during the early seed growth (200 °Cd to 300 °Cd). The dry matter partitioning was not affected by the year. Thus, when dry matter accumulation ceased only 30% in Europe and 27% in Magali of the aboveground dry weight was in the reproductive organs. The mean inflorescence weight reached its maximum after 450 °Cd from inflorescence flowering

Augmenting Experimental Data with Simulations to Improve Activity Classification in Healthcare Monitoring

Human micro-Doppler signatures in most passive WiFi radar (PWR) scenarios are captured through real-world measurements using various hardware platforms. However, gathering large volumes of high quality and diverse real radar datasets has always been an expensive and laborious task. This work presents an open-source motion capture data-driven simulation tool SimHumalator that is able to generate human microDoppler radar data in PWR scenarios. We qualitatively compare the micro-Doppler signatures generated through SimHumalator with the measured real signatures. Here, we present the use of SimHumalator to simulate a set of human actions. We demonstrate that augmenting a measurement database with simulated data, using SimHumalator, results in an 8% improvement in classification accuracy. Our results suggest that simulation data can be used to augment experimental datasets of limited volume to address the cold-start problem typically encountered in radar research

Exploiting semantic and public prior information in MonoSLAM

In this paper, we propose a method to use semantic information to improve the use of map priors in a sparse, feature-based MonoSLAM system. To incorporate the priors, the features in the prior and SLAM maps must be associated with one another. Most existing systems build a map using SLAM and then align it with the prior map. However, this approach assumes that the local map is accurate, and the majority of the features within it can be constrained by the prior. We use the intuition that many prior maps are created to provide semantic information. Therefore, valid associations only exist if the features in the SLAM map arise from the same kind of semantic object as the prior map. Using this intuition, we extend ORB-SLAM2 using an open source pre-trained semantic segmentation network (DeepLabV3+) to incorporate prior information from Open Street Map building footprint data. We show that the amount of drift, before loop closing, is significantly smaller than that for original ORB-SLAM2. Furthermore, we show that when ORB-SLAM2 is used as a prior-aided visual odometry system, the tracking accuracy is equal to or better than the full ORB-SLAM2 system without the need for global mapping or loop closure

Rofecoxib and cardiovascular adverse events in adjuvant treatment of colorectal cancer

Background Selective cyclooxygenase inhibitors may retard the progression of cancer, but they have enhanced thrombotic potential. We report on cardiovascular adverse events in patients receiving rofecoxib to reduce rates of recurrence of colorectal cancer. Methods All serious adverse events that were cardiovascular thrombotic events were reviewed in 2434 patients with stage II or III colorectal cancer participating in a randomized, placebo-controlled trial of rofecoxib, 25 mg daily, started after potentially curative tumor resection and chemotherapy or radiotherapy as indicated. The trial was terminated prematurely owing to worldwide withdrawal of rofecoxib. To examine possible persistent risks, we examined cardiovascular thrombotic events reported up to 24 months after the trial was closed. Results The median duration of active treatment was 7.4 months. The 1167 patients receiving rofecoxib and the 1160 patients receiving placebo were well matched, with a median follow-up period of 33.0 months (interquartile range, 27.6 to 40.1) and 33.4 months (27.7 to 40.4), respectively. Of the 23 confirmed cardiovascular thrombotic events, 16 occurred in the rofecoxib group during or within 14 days after the treatment period, with an estimated relative risk of 2.66 (from the Cox proportional-hazards model; 95% confidence interval [CI], 1.03 to 6.86; P = 0.04). Analysis of the Antiplatelet Trialists’ Collaboration end point (the combined incidence of death from cardiovascular, hemorrhagic, and unknown causes; of nonfatal myocardial infarction; and of nonfatal ischemic and hemorrhagic stroke) gave an unadjusted relative risk of 1.60 (95% CI, 0.57 to 4.51; P = 0.37). Fourteen more cardiovascular thrombotic events, six in the rofecoxib group, were reported within the 2 years after trial closure, with an overall unadjusted relative risk of 1.50 (95% CI, 0.76 to 2.94; P = 0.24). Four patients in the rofecoxib group and two in the placebo group died from thrombotic causes during or within 14 days after the treatment period, and during the follow-up period, one patient in the rofecoxib group and five patients in the placebo group died from cardiovascular causes. Conclusions Rofecoxib therapy was associated with an increased frequency of adverse cardiovascular events among patients with a median study treatment of 7.4 months’ duration. (Current Controlled Trials number, ISRCTN98278138.

Weak in the NEES?: Auto-Tuning Kalman Filters with Bayesian Optimization

ISIF Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. Process noise covariance, pre-whitening filter models for non-white noise, etc. Conventional optimization techniques for tuning can get stuck in poor local minima and can be expensive to implement with real sensor data. To address these issues, a new 'black box' Bayesian optimization strategy is developed for automatically tuning Kalman filters. In this approach, performance is characterized by one of two stochastic objective functions: Normalized estimation error squared (NEES) when ground truth state models are available, or the normalized innovation error squared (NIS) when only sensor data is available. By intelligently sampling the parameter space to both learn and exploit a nonparametric Gaussian process surrogate function for the NEESINIS costs, Bayesian optimization can efficiently identify multiple local minima and provide uncertainty quantification on its results

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

Preconditioning with sevoflurane decreases PECAM-1 expression and improves one-year cardiovascular outcome in coronary artery bypass graft surgery

Background. Cardiac preconditioning is thought to be involved in the observed decreased coronary artery reocclusion rate in patients with angina preceding myocardial infarction. We prospectively examined whether preconditioning by sevoflurane would decrease late cardiac events in patients undergoing coronary artery bypass graft (CABG) surgery. Methods. Seventy-two patients scheduled for elective CABG surgery were randomized to preconditioning by sevoflurane (10 min at 4 vol%) or placebo. For all patients, follow-up of adverse cardiac events was obtained 6 and 12 months after surgery. Transcript levels for platelet-endothelial cell adhesion molecule-1 (PECAM-1/CD31), catalase and heat shock protein 70 (Hsp70) were determined in atrial biopsies after sevoflurane preconditioning. Results. Pharmacological preconditioning by sevoflurane reduced the incidence of late cardiac events during the first year after CABG surgery (sevoflurane 3% vs 17% in the placebo group, log-rank test, P=0.038). One patient in the sevoflurane group and three patients in the placebo group experienced new episodes of congestive heart failure and three additional patients had coronary artery reocclusion. Perioperative peak concentrations for myocardial injury markers were higher in patients with subsequent late cardiac events [NTproBNP, 9031 (4125) vs 3049 (1906) ng litre−1, P<0.001; cTnT, 1.31 (0.88) vs 0.46 (0.29) µg litre−1, P<0.001]. Transcript levels were reduced for PECAM-1 and increased for catalase but unchanged for Hsp70 in atrial biopsies after sevoflurane preconditioning. Conclusions. This prospective randomized clinical study provides evidence of a protective role for pharmacological preconditioning by sevoflurane in late cardiac events in CABG patients, which may be related to favourable transcriptional changes in pro- and antiprotective protein