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

Sensing and perception technology to enable real time monitoring of passenger movement behaviours through congested rail stations

© 2015 ATRF, Commonwealth of Australia. All rights reserved. Passenger behaviour can have a range of effects on rail operations from negative to positive. While rail service providers strive to design and operate systems in a manner that promotes positive passenger behaviour, congestion is a confounding factor, which can create responses that may undermine these efforts. The real time monitoring of passenger movement and behaviour through public transport environments including precincts, concourses, platforms and train vestibules would enable operators to more effectively manage congestion at a whole-of-station level. While existing crowd monitoring technologies allow operators to monitor crowd densities at critical locations and react to overcrowding incidents, they do not necessarily provide an understanding of the cause of such issues. Congestion is a complex phenomenon involving the movements of many people though a set of spaces and monitoring these spaces requires tracking large numbers of individuals. To do this, traditional surveillance technologies might be used but at the expense of introducing privacy concerns. Scalability is also a problem, as complete sensor coverage of entire rail station precinct, concourse and platform areas potentially requires a high number of sensors, increasing costs. In light of this, there is a need for sensing technology that collects data from a set of ‘sparse sensors’, each with a limited field of view, but which is capable of forming a network that can track the movement and behaviour of high numbers of associated individuals in a privacy sensitive manner. This paper presents work towards the core crowd sensing and perception technology needed to enable such a capability. Building on previous research using three-dimensional (3D) depth camera data for person detection, a privacy friendly approach to tracking and recognising individuals is discussed. The use of a head-to-shoulder signature is proposed to enable association between sensors. Our efforts to improve the reliability of this measure for this task are outlined and validated using data captured at Brisbane Central rail station

Sensor registration for robotic applications

Multi-sensor data fusion plays an essential role in most robotic applications. Appropriate registration of information from different sensors is a fundamental requirement in multi-sensor data fusion. Registration requires significant effort particularly when sensor signals do not have direct geometric interpretations, observer dynamics are unknown and occlusions are present. In this paper, we propose Mutual Information (MI) based sensor registration which exploits the effect of a common cause in the observed space on the sensor outputs that does not require any prior knowledge of relative poses of the observers. Simulation results are presented to substantiate the claim that the algorithm is capable of registering the sensors in the presence of substantial observer dynamics. © 2008 Springer-Verlag Berlin Heidelberg

Bootstrapping navigation and path planning using human positional traces

Navigating and path planning in environments with limited a priori knowledge is a fundamental challenge for mobile robots. Robots operating in human-occupied environments must also respect sociocontextual boundaries such as personal workspaces. There is a need for robots to be able to navigate in such environments without having to explore and build an intricate representation of the world. In this paper, a method for supplementing directly observed environmental information with indirect observations of occupied space is presented. The proposed approach enables the online inclusion of novel human positional traces and environment information into a probabilistic framework for path planning. Encapsulation of sociocontextual information, such as identifying areas that people tend to use to move through the environment, is inherently achieved without supervised learning or labelling. Our method bootstraps navigation with indirectly observed sensor data, and leverages the flexibility of the Gaussian process (GP) for producing a navigational map that sampling based path planers such as Probabilistic Roadmaps (PRM) can effectively utilise. Empirical results on a mobile platform demonstrate that a robot can efficiently and socially-appropriately reach a desired goal by exploiting the navigational map in our Bayesian statistical framework. © 2013 IEEE

Mutual information based data association

Relating information originating from disparate sensors without any attempt to model the environment or the behaviour of any particular object within it is a challenging task. Inspired by human perception, the focus of this paper will be on observing objects moving in space using sensors that operate based on different physical principles and the fact that motion has in principle, greater power to specify properties of an object than purely spatial information captured as a single observation in time. The contribution of this paper include the development of a novel strategy for detecting a set of signals that are statistically dependent and correspond to each other related by a common cause. Mutual Information is proposed as a measure of statistical dependence. The algorithm is evaluated through simulations and three application domains, which includes, (1.) Grouping problem in images, (2.) Data association problem in moving observers with dynamic targets, and (3.) Multi-modal sensor fusion. © 2009 IEEE

Socially Constrained Tracking in Crowded Environments Using Shoulder Pose Estimates.

© 2018 IEEE. Detecting and tracking people is a key requirement in the development of robotic technologies intended to operate in human environments. In crowded environments such as train stations this task is particularly challenging due the high numbers of targets and frequent occlusions. In this paper we present a framework for detecting and tracking humans in such crowded environments in terms of 2D pose (x, y, θ). The main contributions are a method for extracting pose from the most visible parts of the body in a crowd, the head and shoulders, and a tracker which leverages social constraints regarding peoples orientation, movement and proximity to one another, to improve robustness in this challenging environment. The framework is evaluated on two datasets: one captured in a lab environment with ground truth obtained using a motion capture system, and the other captured in a busy inner city train station. Pose errors are reported against the ground truth and the tracking results are then compared with a state-of-the-art person tracking framework

Mutual information strategies for sensor registration

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis is concerned with relating information originating from disparate sensors by exploring the statistical dependence between multiple time domain signals that occur as a result of a common cause influencing outputs of the sensors. Relating the signals without any attempt to model the environment or the behaviour of any particular object within it is the main focus of this work as scenarios where sufficient a-priori knowledge is not available is of primary interest. Mutual Information (MI) is selected as a suitable metric for determining statistical dependence mainly due to its ability to identify nonlinear high order e ects, and due to its ability to deal with multi-dimensional input signals with relative ease. Inspired by human perception, the focus will be on observing objects moving in space using sensors that operate based on different physical principles and the fact that motion has in principle, greater power to specify properties of an object than purely spatial information captured as a single observation in time. Our first intention is to utilise the dependence between variables to aid active sensing. The second objective is that of multi-sensor, multi-object tracking which is a challenging problem in large part because of the need to solve the embedded problem of data association, which is the task of relating the measurements from different sensors that correspond to the same object. The contribution of this thesis include the development of a novel strategy for detecting the set of signals that are statistically dependent and correspond to each other from two large multi-dimensional signal streams. The technique is based on deriving a linear mapping that maximises MI between the signal streams in two-dimensional space. The mapping is obtained by an iterative process that maximises MI through using analytical expressions of the gradients of three measures equivalent to two individual entropies and one joint entropy of the signal streams, while at the same time regularising the coefficients of the mapping using L1 and L2 norms. Thus a sparse linear mapping that makes it possible to identify the most mutually informative signal pairings, without the need for exhaustive pair-wise comparisons is obtained. This results in a common multimodal data association methodology, which could be extended to a wide range of sensors with different modalities. The techniques developed are extensively analysed through a series of simulations and experiments. The approach is demonstrated on registration of sensors with disparate modalities, registration of sensors on moving observers and target grouping

Towards more train paths through early passenger intention inference

© 2015 ATRF, Commonwealth of Australia. All rights reserved. In public train stations, the designed way finding tends to induce individuals to conform to specific egress patterns. Whilst this is desirable for a number of reasons, it can cumulate into congestion at specific points in the station. Which, in turn, can increase dwell time; for example, loading and unloading time increases with concentrations of people trying to load/unload onto the same carriage. Clearly, an influencing strategy that is more responsive to the current station situation could have advantages. Our prior research studies in Perth Station demonstrated the feasibility of reliably and predictably influencing passengers egress patterns in real time during operations. This capability suggests the possibility of active counterbalancing of the egress-alternatives while maintaining way finding. However, the prerequisite for such capability is the availability of knowledge of passenger's intention at a point in their journey where viable egress-alternatives to their destination exist. This work details an approach towards an early (in the passenger journey) passenger intention inference system necessary to enable active egress-alternative influencing. Our contextually grounded approach infers intention through reasoning upon observed system and passenger cues in conjunction with a-priori knowledge of how train stations are used. The empirical validation of our intention inference system, which was conducted with data acquired during operations on a platform in Brisbane’s Central train station in Queensland, is presented and discussed. The findings are then employed to argue the feasibility of an influencing system to reduce passenger congestion and the potential service impacts

DEVICES, FRAMEWORKS AND METHODOLOGIES CONFIGURED TO ENABLE AUTOMATED MONITORING AND ANALYSIS OF DWELL TIME

Described herein are devices, frameworks and methodologies configured to enable monitoring and analysis of dwell time in respect of a human conveyance. Embodiments of the invention have been particularly developed for monitoring and analysis of dwell time in respect of trains. In some examples, the technology makes use of depth-sensitive sensor equipment to monitor activity in three dimensions, including train and passenger and activity, thereby to identify artefacts of dwell time events

Motion states inference through 3D shoulder gait analysis and Hierarchical Hidden Markov Models

Automatically inferring human intention from walking movements is an important research concern in robotics and other fields of study. It is generally derived from temporal motion of limb position relative to the body. These changes can also be reected in the change of stance and gait. Conventional systems relying on gait are usually based on tracking the lower body motion (hip, foot) and are extracted from monocular camera data. However, such data can be inaccessible in crowded environments where occlusions of the lower body are prevalent. This paper proposes a novel approach to utilize upper body 3D-motion and Hierarchical Hidden Markov Models to estimate human ambulatory states, such as quietly standing, starting to walk (gait initiation), walking (gait cycle), or stopping (gait termination). Methods have been tested on real data acquired through a motion capture system where foot measurements (heels and toes) were used as ground truth data for labeling the states to train and test the models. Current results demonstrate the feasibility of using such a system to infer lower-body motion states and sub-states through observations of 3D shoulder motion online. Our results enable applications in situations where only upper body motion is readily observable