5 research outputs found
ECMR’13 Special Issue
This special issue contains extended versions of the best papers from the 6th European Conference on Mobile Robots (ECMR). ECMR is a biennial European forum, internationally open, that allows roboticists throughout Europe to become acquainted with the latest research accomplishments and innovations in mobile robotics and mobile human–robot systems. ECMR covers most aspects of mobile robotics research and machine intelligence, including (but not limited to) the following topics: multi-sensor fusion, localization, map building, navigation, active perception, behavior-based robotics, path and task planning, learning and adaptation, robot vision, human–robot interaction, cognitive robotics, experimental evaluation and benchmarking, 3D sensing, and applications of mobile robotics in land, water, air, underground, and space.Peer ReviewedPostprint (author's final draft
Creating Simplified 3D Models with High Quality Textures
This paper presents an extension to the KinectFusion algorithm which allows
creating simplified 3D models with high quality RGB textures. This is achieved
through (i) creating model textures using images from an HD RGB camera that is
calibrated with Kinect depth camera, (ii) using a modified scheme to update
model textures in an asymmetrical colour volume that contains a higher number
of voxels than that of the geometry volume, (iii) simplifying dense polygon
mesh model using quadric-based mesh decimation algorithm, and (iv) creating and
mapping 2D textures to every polygon in the output 3D model. The proposed
method is implemented in real-time by means of GPU parallel processing.
Visualization via ray casting of both geometry and colour volumes provides
users with a real-time feedback of the currently scanned 3D model. Experimental
results show that the proposed method is capable of keeping the model texture
quality even for a heavily decimated model and that, when reconstructing small
objects, photorealistic RGB textures can still be reconstructed.Comment: 2015 International Conference on Digital Image Computing: Techniques
and Applications (DICTA), Page 1 -
Efficient Surfel Fusion Using Normalised Information Distance
We present a new technique that achieves a significant
reduction in the quantity of measurements required for a fusion based dense 3D mapping system to converge to an accurate, de-noised surface reconstruction. This is achieved
through the use of a Normalised Information Distance metric, that computes the novelty of the information contained
in each incoming frame with respect to the reconstruction,
and avoids fusing those frames that exceed a redundancy
threshold. This provides a principled approach for opitmising the trade-off between surface reconstruction accuracy
and the computational cost of processing frames. The technique builds upon the ElasticFusion (EF) algorithm where
we report results of the technique’s scalability and the accuracy of the resultant maps by applying it to both the ICL-NUIM [3] and TUM RGB-D [8] datasets. These results
demonstrate the capabilities of the approach in performing
accurate surface reconstructions whilst utilising a fraction
of the frames when compared to the original EF algorithm
Dense Visual Simultaneous Localisation and Mapping in Collaborative and Outdoor Scenarios
Dense visual simultaneous localisation and mapping (SLAM) systems can produce 3D
reconstructions that are digital facsimiles of the physical space they describe. Systems that
can produce dense maps with this level of fidelity in real time provide foundational spatial
reasoning capabilities for many downstream tasks in autonomous robotics. Over the past
15 years, mapping small scale, indoor environments, such as desks and buildings, with a
single slow moving, hand-held sensor has been one of the central focuses of dense visual
SLAM research.
However, most dense visual SLAM systems exhibit a number of limitations which
mean they cannot be directly applied in collaborative or outdoors settings. The contribution
of this thesis is to address these limitations with the development of new systems and
algorithms for collaborative dense mapping, efficient dense alternation and outdoors
operation with fast camera motion and wide field of view (FOV) cameras. We use
ElasticFusion, a state-of-the-art dense SLAM system, as our starting point where each of
these contributions is implemented as a novel extension to the system.
We first present a collaborative dense SLAM system that allows a number of
cameras starting with unknown initial relative positions to maintain local maps with the
original ElasticFusion algorithm. Visual place recognition across local maps results in
constraints that allow maps to be aligned into a common global reference frame, facilitating
collaborative mapping and tracking of multiple cameras within a shared map.
Within dense alternation based SLAM systems, the standard approach is to fuse
every frame into the dense model without considering whether the information contained
within the frame is already captured by the dense map and therefore redundant. As the
number of cameras or the scale of the map increases, this approach becomes inefficient. In
our second contribution, we address this inefficiency by introducing a novel information
theoretic approach to keyframe selection that allows the system to avoid processing
redundant information. We implement the procedure within ElasticFusion, demonstrating
a marked reduction in the number of frames required by the system to estimate an accurate,
denoised surface reconstruction.
Before dense SLAM techniques can be applied in outdoor scenarios we must
first address their reliance on active depth cameras, and their lack of suitability to fast
camera motion. In our third contribution we present an outdoor dense SLAM system. The system overcomes the need for an active sensor by employing neural network-based depth
inference to predict the geometry of the scene as it appears in each image. To address the
issue of camera tracking during fast motion we employ a hybrid architecture, combining
elements of both dense and sparse SLAM systems to perform camera tracking and to
achieve globally consistent dense mapping.
Automotive applications present a particularly important setting for dense visual
SLAM systems. Such applications are characterised by their use of wide FOV cameras and
are therefore not accurately modelled by the standard pinhole camera model. The fourth
contribution of this thesis is to extend the above hybrid sparse-dense monocular SLAM
system to cater for large FOV fisheye imagery. This is achieved by reformulating the
mapping pipeline in terms of the Kannala-Brandt fisheye camera model. To estimate depth,
we introduce a new version of the PackNet depth estimation neural network (Guizilini et
al., 2020) adapted for fisheye inputs.
To demonstrate the effectiveness of our contributions, we present experimental
results, computed by processing the synthetic ICL-NUIM dataset of Handa et al. (2014) as
well as the real-world TUM-RGBD dataset of Sturm et al. (2012). For outdoor SLAM we
show the results of our system processing the autonomous driving KITTI and KITTI-360
datasets of Geiger et al. (2012a) and Liao et al. (2021) respectively
ECMR’13 Special Issue
This special issue contains extended versions of the best papers from the 6th European Conference on Mobile Robots (ECMR). ECMR is a biennial European forum, internationally open, that allows roboticists throughout Europe to become acquainted with the latest research accomplishments and innovations in mobile robotics and mobile human–robot systems. ECMR covers most aspects of mobile robotics research and machine intelligence, including (but not limited to) the following topics: multi-sensor fusion, localization, map building, navigation, active perception, behavior-based robotics, path and task planning, learning and adaptation, robot vision, human–robot interaction, cognitive robotics, experimental evaluation and benchmarking, 3D sensing, and applications of mobile robotics in land, water, air, underground, and space.Peer Reviewe