Very low complexity convolutional neural network for quadtree structures

© 2018 Australasian Robotics and Automation Association. All rights reserved. In this paper, we present a Very Low Complexity Convolutional Neural Network (VLC-CNN) for the purpose of generating quadtree data structures for image segmentation. The use of quadtrees to encode images has applications including video encoding and robotic perception, with examples including the Coding Tree Unit in the High Efficiency Video Coding (HEVC) standard and Occupancy Grid Maps (OGM) as environment representations with variable grid-size. While some methods for determining quadtree structures include brute-force algorithms or heuristics, this paper describes the use of a Convolutional Neural Network (CNN) to predict the quadtree structure. CNNs traditionally require substantial computational and memory resources to operate, however, VLC-CNN exploits downsampling and integer-only quantised arithmetic to achieve minimal complexity. Therefore, VLC-CNN's minimal design makes it feasible for implementation in realtime or memory-constrained processing applications

Gaussian Markov Random Fields for fusion in information form

© 2016 IEEE. 2.5D maps are preferable for representing the environment owing to their compactness. When noisy observations from multiple diverse sensors at different resolutions are available, the problem of 2.5D mapping turns to how to compound the information in an effective and efficient manner. This paper proposes a generic probabilistic framework for fusing efficiently multiple sources of sensor data to generate amendable, high-resolution 2.5D maps. The key idea is to exploit the sparse structure of the information matrix. Gaussian Markov Random Fields are employed to learn a prior map, which uses the conditional independence property between spatial location to obtain a representation of the state with a sparse information matrix. This prior map encoded in information form can then be updated with other sources of sensor data in constant time. Later, mean state vector and variances can be also efficiently recovered using sparse matrices techniques. The proposed approach allows accurate estimation of 2.5D maps at arbitrary resolution, while incorporating sensor noise and spatial dependency in a statistically sound way. We apply the proposed framework to pipe wall thickness mapping and fuse data from two diverse sensors that have different resolutions. Experimental results are compared with three other methods, showing that, while greatly reducing computation time, the proposed framework is able to capture in large extend the spatial correlation to generate equivalent results to the computationally expensive optimal fusion method in covariance form with a Gaussian Process prior

Simultaneous asynchronous microphone array calibration and sound source localisation

© 2015 IEEE. In this paper, an approach for sound source localisation and calibration of an asynchronous microphone array is proposed to be solved simultaneously. A graph-based Simultaneous Localisation and Mapping (SLAM) method is used for this purpose. Traditional sound source localisation using a microphone array has two main requirements. Firstly, geometrical information of microphone array is needed. Secondly, a multichannel analog-to-digital converter is required to obtain synchronous readings of the audio signal. Recent works aim at releasing these two requirements by estimating the time offset between each pair of microphones. However, it was assumed that the clock timing in each microphone sound card is exactly the same, which requires the clocks in the sound cards to be identically manufactured. A methodology is hereby proposed to calibrate an asynchronous microphone array using a graph-based optimisation method borrowed from the SLAM literature, effectively estimating the array geometry, time offset and clock difference/drift rate of each microphone together with the sound source locations. Simulation and experimental results are presented, which prove the effectiveness of the proposed methodology in achieving accurate estimates of the microphone array characteristics needed to be used on realistic settings with asynchronous sound devices

Kidnapped laser-scanner for evaluation of RFEC tool

© 2015 IEEE. An algorithm is proposed for matching data from different sensing modalities. The problem is formalised as a kidnapped robot problem, where Bayesian fusion is used to find the most likely location where both modalities agree. The key idea of our algorithm is to model the correlation between the two modalities as a likelihood used to update a location prior. Data, in this case, is represented as 2.5D thickness maps from a laser scanner and a Remote Field Eddy Current (RFEC) tool, used in non-destructive testing to assess the condition of infrastructures. The laser data is limited, while RFEC data is continuous. Given some prior in location, the aim is to find the 2.5D thickness map from the laser that corresponds to the RFEC data, which should be noted is highly noisy. Real data from CCTV inspections of water pipes are used to validate the proposed approach

Split conditional independent mapping for sound source localisation with inverse-depth parametrisation

© 2016 IEEE. In this paper, we propose a framework to map stationary sound sources while simultaneously localise a moving robot. Conventional methods for localisation and sound source mapping rely on a microphone array and either, 1) a proprioceptive sensor only (such as wheel odometry) or 2) an additional exteroceptive sensor (such as cameras or lasers) to get accurately the robot locations. Since odometry drifts over time and sound observations are bearing-only, sparse and extremely noisy, the former can only deal with relatively short trajectories before the whole map drifts. In comparison, the latter can get more accurate trajectory estimation over long distances and a better estimation of the sound source map as a result. However, in most of the work in the literature, trajectory estimation and sound source mapping are treated as uncorrelated, which means an update on the robot trajectory does not propagate properly to the sound source map. In this paper, we proposed an efficient method to correlate robot trajectory with sound source mapping by exploiting the conditional independence property between two maps estimated by two different Simultaneous Localisation and Mapping (SLAM) algorithms running in parallel. In our approach, the first map has the flexibility that can be built with any SLAM algorithm (filtering or optimisation) to estimate robot poses with an exteroceptive sensor. The second map is built by using a filtering-based SLAM algorithm locating all stationary sound sources parametrised with Inverse Depth Parametrisation (IDP). Robot locations used during IDP initialisation are the common features shared between the two SLAM maps, which allow to propagate information accordingly. Comprehensive simulations and experimental results show the effectiveness of the proposed method

Modelling in-pipe acoustic signal propagation for condition assessment of multi-layer water pipelines

© 2015 IEEE. A solution to the condition assessment of fluid-filled conduits based on the analysis of in-pipe acoustic signal propagation is presented in this paper. The sensor arrangement consists of an acoustic emitter from which a known sonic pulse is generated, and a collocated hydrophone receiver that records the arrival acoustic wave at a high sampling rate. The proposed method exploits the influence of the surrounding environment on the propagation of an acoustic wave to estimate the condition of the pipeline. Specifically, the propagation speed of an acoustic wave is influenced by the hoop stiffness of the surrounding materials, a fact that has been exploited in the analysis of boreholes in the literature. In this work, this finding is extended to validate the analytical expression derived to infer the condition of uniform, axis-symmetric lined waterworks, a first step to ultimately be able to predict the remaining active life (time-to-failure) of pipelines with arbitrary geometries through finite element analysis (FEA). An investigation of the various aspects of the proposed methodology with typical pipe material and structures is presented to appreciate the advantages of modelling acoustic waves behaviours in fluid-filled cylindrical cavities for condition assessment of water pipelines

Robust Incremental SLAM under Constrained Optimization Formulation

© 2016 IEEE. In this letter, we propose a constrained optimization formulation and a robust incremental framework for the simultaneous localization and mapping problem (SLAM). The new SLAM formulation is derived from the nonlinear least squares (NLS) formulation by mathematically formulating loop-closure cycles as constraints. Under the constrained SLAM formulation, we study the robustness of an incremental SLAM algorithm against local minima and outliers as a constraint/loop-closure cycle selection problem. We find a constraint metric that can predict the objective function growth after including the constraint. By the virtue of the constraint metric, we select constraints into the incremental SLAM according to a least objective function growth principle to increase robustness against local minima and perform χ 2 difference test on the constraint metric to increase robustness against outliers. Finally, using sequential quadratic programming (SQP) as the solver, an incremental SLAM algorithm (iSQP) is proposed. Experimental validations are provided to illustrate the accuracy of the constraint metric and the robustness of the proposed incremental SLAM algorithm. Nonetheless, the proposed approach is currently confined to datasets with sparse loop-closures due to its computational cost

Real-time sound source localisation for target tracking applications using an asynchronous microphone array

© 2015 IEEE. This paper presents a strategy for sound source localisation using an asynchronous microphone array. The proposed method is suitable for target tracking applications, in which the sound source with a known frequency is attached to the target. Conventional microphone array technologies require a multi-channel A/D converter for inter-microphone synchronization making the technology relatively expensive. In this work, the requirement of synchronization between channels is relaxed by adding an external reference audio signal. The only assumption is that the frequencies of the reference signal and the sound source attached to the target are fixed and known beforehand. By exploiting the information provided by the known reference signal, the Direction Of Arrival (DOA) of target sound source can be calculated in real-time. The key idea of the algorithm is to use the reference source to 'pseudo-align' the audio signals from different channels. Once the channels are 'pseudo-aligned', a dedicated DOA estimation method based on Time Difference Of Arrival (TDOA) can be employed to find the relative bearing information between the target sound source and microphone array. Due to the narrow band of frequency of target sound source, the proposed approach is proven to be robust to low signals-to-noise ratios. Comprehensive simulations and experimental results are presented to show the validity of the algorithm

Towards real-time 3D sound sources mapping with linear microphone arrays

© 2017 IEEE. In this paper, we present a method for real-time 3D sound sources mapping using an off-the-shelf robotic perception sensor equipped with a linear microphone array. Conventional approaches to map sound sources in 3D scenarios use dedicated 3D microphone arrays, as this type of arrays provide two degrees of freedom (DOF) observations. Our method addresses the problem of 3D sound sources mapping using a linear microphone array, which only provides one DOF observations making the estimation of the sound sources location more challenging. In the proposed method, multi hypotheses tracking is combined with a new sound source parametrisation to provide with a good initial guess for an online optimisation strategy. A joint optimisation is carried out to estimate 6 DOF sensor poses and 3 DOF landmarks together with the sound sources locations. Additionally, a dedicated sensor model is proposed to accurately model the noise of the Direction of Arrival (DOA) observation when using a linear microphone array. Comprehensive simulation and experimental results show the effectiveness of the proposed method. In addition, a real-time implementation of our method has been made available as open source software for the benefit of the community