Similarity and location-based real-time loop closure : SNAPS for SLAM in unexplored-environments

Loop closure is an inseparable part of any accurate and reliable visual simultaneous localization and mapping (SLAM) algorithm for autonomous vehicles and mobile robots. Loop closure potentially decreases the impact of the cumulative drift while generating the map of the traversed environment. In this paper, a heuristic similarity and location-based approach for loop closure in unexplored environments is introduced. The current SLAM implementation on average requires 0.295 seconds per frame from which only 0.0270 seconds are the runtime latencies of the similarity and location-based real-time loop closure (SNAPS), which includes trajectory correction. The proposed approach results in a 65% decrease in the mean deviation from the ground truth. In the conducted study, neither conventional bag-of-words models, nor computationally expensive deep neural networks have been used to detect and perform loop closure, which makes the proposed approach both interpretable and efficient. In fact, we propose a method which tries to find loop closure candidates based on the location and also an interpretable similarity score attained from the generated thumbnails of the read frames instead of the local descriptors. Additionally, the employed discount factor applied on the pose trajectory update rule guarantees a consistent and accurate map. Lastly, the KITTI dataset is used to demonstrate the efficiency and accuracy of SNAPS for SLAM

Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input

Generalized Mixed Integer Rounding Valid Inequalities

We present new families of valid inequalities for (mixed) integer programming (MIP) problems. These valid inequalities are based on a generalization of the 2-step mixed integer rounding (MIR), proposed by Dash and Günlük (2006). We prove that for any positive integer n, n facets of a certain (n + 1)-dimensional mixed integer set can be obtained through a process which includes n consecutive applications of MIR. We then show that for any n, the last of these facets, the n-step MIR facet, can be used to generate a family of valid inequalities for the feasible set of a general (mixed) IP constraint, the n-step MIR inequalities. The Gomory Mixed Integer Cut and the 2-step MIR inequality of Dash and Günlük (2006) are simply the first two families corresponding to n = 1, 2, respectively. The n-step MIR inequalities are easily produced using closed-form periodic functions, which we refer to as the n-step MIR functions. None of these functions dominates the other on its whole period. Moreover, for any n, the n-step MIR inequalities define new families of two-slope facets for the finite and the infinite group problems.

Augmented reality for the visually impaired : navigation aid and scene semantics for indoor use cases

With the Augmented Reality (AR) technology available today, it is quite feasible to accommodate the needs of the visually impaired (VI) via AR. In this paper, a framework is introduced to help the VI navigate and explore unfamiliar indoor environments. In contrast to commonly used AR applications focused on visual augmentation, the proposed framework employs auditory three-dimensional feedback (A3DF) for guiding the VI. Concretely, the current framework reads the pose of the user and helps the VI reach a target location via A3DF. The A3DF is implemented with the Unity game engine to provide the optimal user experience. After acquiring the environment mesh (EM), the optimal path from the user’s location to the target location is calculated, while avoiding obstacles using Unity’s navigation system. Moreover, the user is provided with semantic information about the unknown environment whilst exploring via auditory information. This framework is implemented on Microsoft HoloLens 2 and tested at an office environment with different locations of interest. Additionally, this framework potentially accelerates the learning curve since the user can be trained using Unity’s simulation environment. Lastly, given different design parameters of the framework, the proposed method can be fine-tuned to fit the specific needs of the individua