Robust Object Classification Approach using Spherical Harmonics

Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise or outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying 3D objects. The proposed framework first uses the voxel grid of concentric spheres to learn features over the unit ball. We then limit the spherical harmonics order level to suppress the effect of noise and outliers. In addition, the entire classification operation is performed in the Fourier domain. As a result, our proposed model learned features that are less sensitive to data perturbations and corruptions. We tested our proposed model against several types of data perturbations and corruptions, such as noise and outliers. Our results show that the proposed model has fewer parameters, competes with state-of-art networks in terms of robustness to data inaccuracies, and is faster than other robust methods. Our implementation code is also publicly available1

Robust pooling through the data mode: Robust Point cloud Classification and Segmentation Through Mode Pooling

The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence the performance of state-of-the-art deep learning networks and their ability to classify or segment objects. While there are some robust deep-learning approaches, they are computationally too expensive for real-time applications. This paper proposes a deep learning solution that includes novel robust pooling layers which greatly enhance network robustness and perform significantly faster than state-of-the-art approaches. The proposed pooling layers replace conventional pooling layers in networks with global pooling operations such as PointNet and DGCNN. The proposed pooling layers look for data mode/cluster using two methods, RANSAC, and histogram, as clusters are indicative of models. We tested the proposed pooling layers on several tasks such as classification, part segmentation, and points normal vector estimation. The results show excellent robustness to high levels of data corruption with less computational requirements as compared to robust state-of-the-art methods. our code can be found at https://github.com/AymanMukh/ModePooling

Missing data compensation for safety-critical components in a drive-by-wire system

In this article, a new multistep ahead predictive filtering scheme is introduced. The proposed technique is essential for proper operation of safety-critical components in a drive-by-wire car. Because limited computational time and memory are available in drive-by-wire systems, the main advantage of our scheme is that it only requires one set of finite impulse response (FIR) filter weights to be tuned while it can be used for different numbers of steps ahead predictions. To verify and compare the proposed filter, our model and four competing methods were applied to predict up to four missing samples of displacement sensor data from a brake pedal in a brake-by-wire system. Experimental results show that prediction performance of our proposed FIR filter is higher than, or at least comparable to, other filters with the same memory requirements and the computational overhead of data missing handling by our proposed method is considerably lower than other comparable methods. Hence, the proposed filter has a superior performance in missing data compensation for drive-by-wire systems

Nonlinearity in an electromechanical braking system: development of a smart caliper

In electromechanical brakes, central controllers require accurate information about the clamp force between brake pad and disc as a function of pad displacement. This function is usually denoted as characteristic curve of the caliper. In a typical electromechanical braking system, clamp force measurements vary with actuator displacements in a hysteretic manner. Due to ageing, temperature and other environmental variations, the hysteretic characteristic curve of calliper varies with time. Therefore, automatic caliper calibration in real time is vital for high-performance braking action and vehicle safety. Due to memory and processing power limitations, the calibration technique should be memory efficient and of low computational complexity. This chapter investigates the hysteresis as a nonlinear effect in the electromechanical brakes, and describes a technique to parametrically model this effect. This technique is a simple and memory-efficient real-time calibration method in which a Maxwell-slip model is fitted to the data samples around each hysteresis cycle. Experimental results from the data recorded in various temperatures show that this technique results in clamp force measurements with less than 0. 7% error over the range of clamp force variations. It is also shown that by using these measurements, the characteristic curve can be accurately calibrated in real time

Robust segmentation of visual data using ranked unbiased scale estimate

A method of data segmentation, based upon robust least K-th order statistical model fitting (LKS), is proposed and applied to image motion and range data segmentation. The estimation method differs from other approaches using versions of LKS in a number of important ways. Firstly, the value of K is not determined by a complex optimization routine. Secondly, having chosen a fit, the estimation of scale of the noise is not based upon the K-th order statistic of the residuals. Other aspects of the full segmentation scheme include the use of segment contiguity to: (a) reduce the number of random sample fits used in the LKS stage, and (b) to “fill-in” holes caused by isolated miss-classified data.Alireza Bab-Hadiashar and David Sute

Fusion of Redundant Information in Brake-By-Wire Systems Using a Fuzzy Voter

In safety critical systems such as brake-by-wire, fault tolerance is usually provided by virtue of having redundant sensors and processing hardware. The redundant information provided by such components should be properly fused to achieve a reliable estimate of the safety critical variable that is sensed or processed by the redundant sensors or hardware. Voting methods are well-known solutions for this category of fusion problems. In this paper, a new voting method, using a fuzzy system for decision-making, is presented. The voted output of the proposed scheme is a weighted average of the sensors signals where the weights are calculated based on the antecedents and consequences of some fuzzy rules in a rulebase. In a case study, we have tested the fuzzy voter along with the well-known majority voting method for a by-wire brake pedal that is equipped with a displacement sensor and two force sensors. Our experimental results show that the performance of the proposed voting method is desirable in the presence of short circuits to ground or supply, excessive noise and sensor drifts. Voting error (in terms of mean square error) is reduced by 82% by the proposed fuzzy voting method, compared to majority voting

Bridging parameter and data spaces for fast robust estimation in computer vision

All high breakdown robust estimators, at their core, include an isolated search in either the data or the parameter space. In this paper, we devise a high breakdown robust estimation technique, called fast least k-th order statistics (FLkOS) that employs the derivatives of order statistics of squared residuals to implement Newton's optimization method for its search. It is mathematically shown that Newton's optimization of the order statistics leads to a very simple and substantially fast search algorithm that bridges the data and parameter spaces. The proposed search involves replacing a p-tuple with another p-tuple in the data space, while moving towards the minimum point of the estimator's cost function in the parameter space. An important practical implication of this strategy is that we can limit the required search in the parameter space to the specific manifold spanned by data. FLkOS is shown to be an effective tool to perform multi-structured data fitting and segmentation via a number of experiments including range image segmentation experiments involving both synthetic and real images and fundamental matrix estimation involving real image pairs. The results show that FLkOS is remarkably efficient and substantially faster than state-of-the-art high breakdown estimators

Multi-Bernoulli sample consensus for simultaneous robust fitting of multiple structures in machine vision

In many image processing applications, such as parametric range and motion segmentation, multiple instances of a model are fitted to data points. The most common robust fitting method, RANSAC, and its extensions are normally devised to segment the structures sequentially, treating the points belonging to other structures as outliers. Thus, the ratio of inliers is small and successful fitting requires a very large number of random samples, incurring cumbrous computation. This paper presents a new method to simultaneously fit multiple structures to data points in a single run. We model the parameters of multiple structures as a random finite set with multi-Bernoulli distribution. Simultaneous search for all structure parameters is performed by Bayesian update of the multi-Bernoulli parameters. Experiments involving segmentation of numerous structures show that our method outperforms well-known methods in terms of estimation error and computational cost. The fast convergence and high accuracy of our method make it an excellent choice for real-time estimation and segmentation of multiple structures in image processing applications

Consistency of robust estimators in multi-structual visual data segmentation

A theoretical framework is presented to study the consistency of robust estimators used in vision problems involving extraction of fine details. A strong correlation between asymptotic performance of a robust estimator and the asymptotic bias of its scale estimate is mathematically demonstrated where the structures are assumed to be linear corrupted by Gaussian noise. A new measure for the inconsistency of scale estimators is defined and formulated by deriving the functional forms of four recent high-breakdown robust estimators. For each estimator, the inconsistency measures are numerically evaluated for a range of mutual distances between structures and inlier ratios, and the minimum mutual distance between the structures, for which each estimator returns a non-bridging fit, is calculated