Author name cluster

James Patrick Underwood

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

16 papers

1 author row

ICRA Conference 2018 Conference Paper

Object Detection for Cattle Gait Tracking

John Gardenier
James Patrick Underwood
Cameron E. F. Clark

Lameness in cattle is a health issue where gait is modified to minimise pain. Cattle are currently visually assessed for locomotion score, which provides the degree of lameness for individual animals. This subjective method is costly in terms of labour, and its level of accuracy and ability to detect small changes in locomotion that is critical for early detection of lameness and associated intervention. Current automatic lameness detection systems found in literature have not yet met the ultimate goal of widespread commercial adoption. We present a sensor configuration to record cattle kinematics towards automatic lameness detection. This configuration features four Time of Flight sensors to view cattle from above and from one side as they exit an automatic rotary milking dairy. Two dimensional near infrared images sampled from 223 cows passing through the system were used to train a Faster R-CNN to detect hooves (F1-score = 0. 90) and carpal/tarsal joints (Fl-score = 0. 85). The depth images were used to project these detected key points into Cartesian space where they were tracked to obtain individual trajectories per limb. The results show that kinematic gait features can be successfully obtained as a first and important step towards objective, accurate, automatic lameness detection.

ICRA Conference 2017 Conference Paper

Deep fruit detection in orchards

Suchet Bargoti
James Patrick Underwood

An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of > 0. 9 achieved for apples and mangoes.

ICRA Conference 2016 Conference Paper

Image classification with orchard metadata

Suchet Bargoti
James Patrick Underwood

Low cost and easy to use monocular vision systems are able to capture large scale, dense data in orchards, to facilitate precision agriculture applications. Accurate image parsing is required for this purpose, however, operating in natural outdoor conditions makes this a complex task due to the undesirable intra-class variations caused by changes in illumination, pose and tree types, etc. Typically these variations are difficult to explicitly model and discriminative classifiers strive to be invariant to them. However, given the presence of structure, in both the orchard and how the data was obtained, a subset of these factors of variations can correlate with readily available metadata, including extrinsic experimental information such as the sun incidence angle, position within farm, etc. This paper presents a method to incorporate such metadata to aid scene parsing based on a multi-scale Multi-Layered Perceptron (MLP) architecture. Experimental results are shown for pixel segmentation over data collected at an apple orchard, leading to fruit detection and yield estimation. The results show a consistent improvement in segmentation accuracy with the inclusion of metadata under different network complexities, training configurations and evaluation metrics.

ICRA Conference 2016 Conference Paper

Self-supervised weed detection in vegetable crops using ground based hyperspectral imaging

Alexander Wendel
James Patrick Underwood

A critical step in treating or eradicating weed infestations amongst vegetable crops is the ability to accurately and reliably discriminate weeds from crops. In recent times, high spatial resolution hyperspectral imaging data from ground based platforms have shown particular promise in this application. Using spectral vegetation signatures to discriminate between crop and weed species has been demonstrated on several occasions in the literature over the past 15 years. A number of authors demonstrated successful per-pixel classification with accuracies of over 80%. However, the vast majority of the related literature uses supervised methods, where training datasets have been manually compiled. In practice, static training data can be particularly susceptible to temporal variability due to physiological or environmental change. A self-supervised training method that leverages prior knowledge about seeding patterns in vegetable fields has recently been introduced in the context of RGB imaging, allowing the classifier to continually update weed appearance models as conditions change. This paper combines and extends these methods to provide a self-supervised framework for hyperspectral crop/weed discrimination with prior knowledge of seeding patterns using an autonomous mobile ground vehicle. Experimental results in corn crop rows demonstrate the system's performance and limitations.

ICRA Conference 2013 Conference Paper

Explicit 3D change detection using ray-tracing in spherical coordinates

James Patrick Underwood
D. Gillsjo
Tim Bailey
Vsevolod Vlaskine

Change detection is important for autonomous perception systems that operate in dynamic environments. Mapping and tracking components commonly handle two ends of the dynamic spectrum: stationarity and rapid motion. This paper presents a fast algorithm for 3D change detection from LIDAR or equivalent optical range sensors, that can operate from arbitrary viewpoints and can detect fast and slow dynamics. Distinct from prior work, the method explicitly detects changes in the world, and suppresses apparent changes in the data due to exploration at frontiers or behind occlusions. Comprehensive experimentation is performed to assess the performance in several application domains. Sample data and source code are provided.

ICRA Conference 2013 Conference Paper

Multi-sensor identity tracking with event graphs

Peter Morton
Bertrand Douillard
James Patrick Underwood

The ability to track moving objects is a key part of autonomous robot operation in real-world environments. Whilst for many tasks knowing the positions of objects may be sufficient, tracking the identity of targets may also be desirable. When objects are well separated preserving identities is trivial, however, the identities of objects that pass close to one another may become confused. This paper considers methods to maintain the identities of tracked objects using a combination of LIDAR and video data. When objects are well separated, they are tracked using location information from the LIDAR. When objects move together and their identities cannot be resolved, interactions are recorded and later resolved using appearance models. A vision based approach is adapted for use with LIDAR data and a new method for identity reasoning is proposed. The methods are validated on a dataset comprising a total of 37906 manually labelled point cloud segments.

IROS Conference 2013 Conference Paper

Orchard fruit segmentation using multi-spectral feature learning

Calvin Hung
Juan I. Nieto 0001
Zachary Taylor
James Patrick Underwood
Salah Sukkarieh

This paper presents a multi-class image segmentation approach to automate fruit segmentation. A feature learning algorithm combined with a conditional random field is applied to multi-spectral image data. Current classification methods used in agriculture scenarios tend to use hand crafted application-based features. In contrast, our approach uses unsupervised feature learning to automatically capture most relevant features from the data. This property makes our approach robust against variance in canopy trees and therefore has the potential to be applied to different domains. The proposed algorithm is applied to a fruit segmentation problem for a robotic agricultural surveillance mission, aiming to provide yield estimation with high accuracy and robustness against fruit variance. Experimental results with data collected in an almond farm are shown. The segmentation is performed with features extracted from multi-spectral (colour and infrared) data. We achieve a global classification accuracy of 88%.

ICRA Conference 2012 Conference Paper

An occlusion-aware feature for range images

Alastair James Quadros
James Patrick Underwood
Bertrand Douillard

This paper presents a novel local feature for 3D range image data called `the line image'. It is designed to be highly viewpoint invariant by exploiting the range image to efficiently detect 3D occupancy, producing a representation of the surface, occlusions and empty spaces. We also propose a strategy for defining keypoints with stable orientations which define regions of interest in the scan for feature computation. The feature is applied to the task of object classification on sparse urban data taken with a Velodyne laser scanner, producing good results.

ICRA Conference 2012 Conference Paper

Scan segments matching for pairwise 3D alignment

Bertrand Douillard
Alastair James Quadros
Peter Morton
James Patrick Underwood
Mark De Deuge
S. Hugosson
M. Hallstrom
Tim Bailey

This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives [10], [19]). The method is tested on seven sequences of Velodyne scans acquired in urban environments. Unlike various other standard versions of ICP, which fail to recover correct alignment when the displacement between scans increases, the proposed method is shown to be robust to displacements of several meters. In addition, it is shown to lead to savings in computational times which are potentially critical in real-time applications.

IROS Conference 2011 Conference Paper

Combining radar and vision for self-supervised ground segmentation in outdoor environments

Annalisa Milella
Giulio Reina
James Patrick Underwood
Bertrand Douillard

Ground segmentation is critical for a mobile robot to successfully accomplish its tasks in challenging environments. In this paper, we propose a self-supervised radar-vision classification system that allows an autonomous vehicle, operating in natural terrains, to automatically construct online a visual model of the ground and perform accurate ground segmentation. The system features two main phases: the training phase and the classification phase. The training stage relies on radar measurements to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground can be used to perform high level tasks such as image segmentation and terrain classification, as well as to solve radar ambiguities. The proposed method leads to the following main advantages: (a) a self-supervised training of the visual classifier, where the radar allows the vehicle to automatically acquire a set of ground samples, eliminating the need for time-consuming manual labeling; (b) the ground model can be continuously updated during the operation of the vehicle, thus making it feasible the use of the system in long range and long duration navigation applications. This paper details the proposed system and presents the results of experimental tests conducted in the field by using an unmanned vehicle.

ICRA Conference 2011 Conference Paper

On the segmentation of 3D LIDAR point clouds

Bertrand Douillard
James Patrick Underwood
Noah Kuntz
Vsevolod Vlaskine
Alastair James Quadros
Peter Morton
Alon Frenkel

This paper presents a set of segmentation methods for various types of 3D point clouds. Segmentation of dense 3D data (e. g. Riegl scans) is optimised via a simple yet efficient voxelisation of the space. Prior ground extraction is empirically shown to significantly improve segmentation performance. Segmentation of sparse 3D data (e. g. Velodyne scans) is addressed using ground models of non-constant resolution either providing a continuous probabilistic surface or a terrain mesh built from the structure of a range image, both representations providing close to real-time performance. All the algorithms are tested on several hand labeled data sets using two novel metrics for segmentation evaluation.

IROS Conference 2010 Conference Paper

Hybrid elevation maps: 3D surface models for segmentation

Bertrand Douillard
James Patrick Underwood
Narek Melkumyan
Surya P. N. Singh
Shrihari Vasudevan
Christopher Joseph Brunner
Alastair James Quadros

This paper presents an algorithm for segmenting 3D point clouds. It extends terrain elevation models by incorporating two types of representations: (1) ground representations based on averaging the height in the point cloud, (2) object models based on a voxelisation of the point cloud. The approach is deployed on Riegl data (dense 3D laser data) acquired in a campus type of environment and compared against six other terrain models. Amongst elevation models, it is shown to provide the best fit to the data as well as being unique in the sense that it jointly performs ground extraction, overhang representation and 3D segmentation. We experimentally demonstrate that the resulting model is also applicable to path planning.

ICRA Conference 2009 Conference Paper

Dynamic path planning with multi-agent data fusion - The Parallel Hierarchical Replanner

Thomas Allen
Andrew John Hill
James Patrick Underwood
Steve Scheding

The design of a hierarchical planning system in which each level operates in parallel and communicates asynchronously is presented. It is shown that this Parallel Hierarchical Replanner is both reactive, and as close to optimal over all information in the state space as is possible given finite computational power. A comparison with three other hierarchical methods is presented, which demonstrates that for scenarios in which the time taken to achieve a mission goal is of greater importance than the cost incurred, this approach has better performance than related methods in the literature.

IROS Conference 2009 Conference Paper

Towards reliable perception for Unmanned Ground Vehicles in challenging conditions

Thierry Peynot
James Patrick Underwood
Steve Scheding

This work aims to promote reliability and integrity in autonomous perceptual systems, with a focus on outdoor unmanned ground vehicle (UGV) autonomy. For this purpose, a comprehensive UGV system, comprising many different exteroceptive and proprioceptive sensors has been built. The first contribution of this work is a large, accurately calibrated and synchronised, multi-modal data-set, gathered in controlled environmental conditions, including the presence of dust, smoke and rain. The data have then been used to analyse the effects of such challenging conditions on perception and to identify common perceptual failures. The second contribution is a presentation of methods for mitigating these failures to promote perceptual integrity in adverse environmental conditions.

IROS Conference 2007 Conference Paper

Calibration of range sensor pose on mobile platforms

James Patrick Underwood
Andrew John Hill
Steve Scheding

This paper describes a new methodology for calculating the translational and rotational offsets of a range sensor to a reference coordinate frame on the platform to which it is affixed. The technique consists of observing an environment of known or partially known geometry, from which the offsets are determined by minimizing the error between the sensed data and the known structure. Analytic results are presented which derive the necessary conditions for a successful optimisation. Practical results confirm the analysis and show that it is possible to obtain more precise results than those obtained through hand measurement.

IROS Conference 2006 Conference Paper

Mutual Information based Sensor Registration and Calibration

Alen Alempijevic
Sarath Kodagoda
James Patrick Underwood
Suresh Kumar
Gamini Dissanayake

Knowledge of calibration, that defines the location of sensors relative to each other, and registration, that relates sensor response due to the same physical phenomena, are essential in order to be able to fuse information from multiple sensors. In this paper, a mutual information (MI) based approach for automatic sensor registration and calibration is presented. Unsupervised learning of a nonparametric sensing model by maximizing mutual information between signal streams is used to relate information from different sensors, allowing unknown sensor registration and calibration to be determined. Experiments conducted in an office environment are used to illustrate the effectiveness of the proposed technique. Two laser sensors are used to capture people mobbing in an arbitrarily manner in the environment and MI from a number of attributes of the motion are used for relating the signal streams from the sensors. Thus the sensor registration and calibration is achieved without using artificial patterns or pre-specified motions