Author name cluster

Peter Morton

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.

3 papers

1 author row

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.

Details

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.

Details

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.

Details