Creating a global vision for sustainable fashion

Textiles, the fastest growing sector in household waste, have created an exponential rise in the export of second hand clothes (SHC) to overseas markets such as Kenya and Tanzania. Despite the few advantages for the destination markets (eg, enterprise opportunities), this has exasperated a difficult situation for domestic production. Increased cheap imports from Asia have also led to decline in SHC markets, resulting in increased land filling and the associated environmental impacts. Our research proposes remanufacturing fashion from the unwanted SHC, embellishing using local (destination market) craft/design. From literature review conducted, reuse and remanufacture of clothing causes the least impact on energy use and appears to be the most environmentally and socially friendly approach to sustainability efforts. Remanufacture of clothing is currently practiced at niche market levels, for it to have a broader impact; it needs to gain entry into the mass-market retail arena. In the mass market arena, the apparel value chain is organized around several parts with a marketing network at the retail level. Lead firms predominantly construct these value chains, are predominantly located in developed countries, and may be large retailers and brand-name firms, playing a significant role in specifying what is to be produced, how, and by whom. Our goal is to understand how designers, manufacturers and retailers may work together in a remanufacturing process. We present findings from interviews with Tanzanian second hand clothes retailers and artisans, UK fashion remanufacturers and retailers. We discuss the implications on the fashion design process and propose a new product development method for sustainable consumption of fashion. We conclude by reflecting on potential mechanisms of the supply chain integration and how the large multinationals may become engaged. Key words: remanufacturing, design process, supply chain, second hand clothe

Convergence analysis for extended Kalman filter based SLAM

The main contribution of this paper is a theoretical analysis of the Extended Kalman Filter (EKF) based solution to the simultaneous localisation and mapping (SLAM) problem. The convergence properties for the general nonlinear two-dimensional SLAM are provided. The proofs clearly show that the robot orientation error has a significant effect on the limit and/or the lower bound of the uncertainty of the landmark location estimates. Furthermore, some insights to the performance of EKF SLAM and a theoretical analysis on the inconsistencies in EKF SLAM that have been recently observed are given. © 2006 IEEE

Automatic fine motor control behaviours for autonomous mobile agents operating on uneven terrains

A novel mechanism able to produce increasingly stable paths for mobile robotic agents travelling over uneven terrain is proposed in this paper. In doing so, cognitive agents can focus on higher-level goal planning, with the increased confidence the resulting tasks will be automatically accomplished via safe and reliable paths within the lower-level skills of the platform. The strategy proposes the extension of the Fast Marching level-set method of propagating interfaces in 3D lattices with a metric to reduce robot body instability. This is particularly relevant for kinematically reconfigurable platforms which significantly modify their mass distribution through posture adaptation, such as humanoids or mobile robots equipped with manipulator arms or varying traction arrangements. Simulation results of an existing reconfigurable mobile rescue robot operating on real scenarios illustrate the validity of the proposed strategy. Copyright 2010 ACM

Convergence and consistency analysis for extended Kalman filter based SLAM

This paper investigates the convergence properties and consistency of Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and- bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided. © 2007 IEEE

An efficient algorithm for line extraction from laser scans

In this paper, an algorithm for extracting line segments from information gathered by a laser rangefinder is presented. The range scan is processed to compute a parameter that is invariant to the position and orientation of straight lines present. This parameter is then used to identify observations that potentially belong to straight lines and compute the slope of these lines. Log-Hough transform, that only explores a small region of the Hough space identified by the slopes computed, is then used to find the equations of the lines present. The proposed method thus combines robustness of the Hough transform technique with the inherent efficiency of line fitting strategies while carrying out all computation in the sensor coordinate frame yielding a fast and robust algorithm for line extraction from laser range scans. Two practical examples are presented to demonstrate the efficacy of the algorithm and compare its performance to the traditional techniques

Eliminating Scale Drift in Monocular SLAM Using Depth from Defocus

© 2017 IEEE. This letter presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene

Monocular 3D metric scale reconstruction using depth from defocus and image velocity

© 2017 IEEE. This paper presents a novel approach to metric scale reconstruction of a three-dimensional (3D) scene using a monocular camera. Using a sequence of images from a monocular camera with a fixed focus lens, metric distance to a set of features in the environment is estimated from image blur due to defocus. The blur texture ambiguity which causes scale errors in depth from defocus is corrected in an EKF framework that exploits image velocity measurements. We show in real experiments that our method converges to a metric scale, accurate, sparse depth map and 3D camera poses with images from a monocular camera. Therefore, the proposed approach has the potential to enhance robot navigation algorithms that rely on monocular cameras

Evaluation of Pose Only SLAM

In recent SLAM (simultaneous localization and mapping) literature, Pose Only optimization methods have become increasingly popular. This is greatly supported by the fact that these algorithms are computationally more efficient, as they focus more on the robots trajectory rather than dealing with a complex map. Implementation simplicity allows these to handle both 2D and 3D environments with ease. This paper presents a detailed evaluation on the reliability and accuracy of Pose Only SLAM, and aims at providing a definitive answer to whether optimizing poses is more advantages than optimizing features. Focus is centered around TORO, a Tree based network optimization algorithm, which has gained increased recognition within the robotics community. We compare this with Least Squares, which is often considered one of the best Maximum Likelihood method available. Results are based on both simulated and real 2D environments, and presented in a way where our conclusions can be substantiated. ©2010 IEEE

3D I-SLSJF: A consistent sparse local submap joining algorithm for building large-scale 3D map

This paper presents an efficient and reliable algorithm for autonomous robots to build large-scale three dimensional maps by combining small local submaps. The algorithm is a generalization of our recent work on two dimensional map joining algorithm - Iterated Sparse Local Submap Joining Filter (I-SLSJF). The 3D local submap joining problem is formulated as a least squares optimization problem and solved by Extended Information Filter (EIF) together with smoothing and iterations. The resulting information matrix is exactly sparse and this makes the algorithm efficient. The smoothing and iteration steps improve the accuracy and consistency of the estimate. The consistency and efficiency of 3D I-SLSJF is demonstrated by comparing the algorithm with some existing algorithms using computer simulations. ©2009 IEEE