11 research outputs found
Robust Photogeometric Localization over Time for Map-Centric Loop Closure
Map-centric SLAM is emerging as an alternative of conventional graph-based
SLAM for its accuracy and efficiency in long-term mapping problems. However, in
map-centric SLAM, the process of loop closure differs from that of conventional
SLAM and the result of incorrect loop closure is more destructive and is not
reversible. In this paper, we present a tightly coupled photogeometric metric
localization for the loop closure problem in map-centric SLAM. In particular,
our method combines complementary constraints from LiDAR and camera sensors,
and validates loop closure candidates with sequential observations. The
proposed method provides a visual evidence-based outlier rejection where
failures caused by either place recognition or localization outliers can be
effectively removed. We demonstrate the proposed method is not only more
accurate than the conventional global ICP methods but is also robust to
incorrect initial pose guesses.Comment: To Appear in IEEE ROBOTICS AND AUTOMATION LETTERS, ACCEPTED JANUARY
201
Robust Place Recognition using an Imaging Lidar
We propose a methodology for robust, real-time place recognition using an
imaging lidar, which yields image-quality high-resolution 3D point clouds.
Utilizing the intensity readings of an imaging lidar, we project the point
cloud and obtain an intensity image. ORB feature descriptors are extracted from
the image and encoded into a bag-of-words vector. The vector, used to identify
the point cloud, is inserted into a database that is maintained by DBoW for
fast place recognition queries. The returned candidate is further validated by
matching visual feature descriptors. To reject matching outliers, we apply PnP,
which minimizes the reprojection error of visual features' positions in
Euclidean space with their correspondences in 2D image space, using RANSAC.
Combining the advantages from both camera and lidar-based place recognition
approaches, our method is truly rotation-invariant and can tackle reverse
revisiting and upside-down revisiting. The proposed method is evaluated on
datasets gathered from a variety of platforms over different scales and
environments. Our implementation is available at
https://git.io/imaging-lidar-place-recognitionComment: ICRA 202
Kidnapped Radar: Topological Radar Localisation using Rotationally-Invariant Metric Learning
This paper presents a system for robust, large-scale topological localisation
using Frequency-Modulated Continuous-Wave (FMCW) scanning radar. We learn a
metric space for embedding polar radar scans using CNN and NetVLAD
architectures traditionally applied to the visual domain. However, we tailor
the feature extraction for more suitability to the polar nature of radar scan
formation using cylindrical convolutions, anti-aliasing blurring, and
azimuth-wise max-pooling; all in order to bolster the rotational invariance.
The enforced metric space is then used to encode a reference trajectory,
serving as a map, which is queried for nearest neighbours (NNs) for recognition
of places at run-time. We demonstrate the performance of our topological
localisation system over the course of many repeat forays using the largest
radar-focused mobile autonomy dataset released to date, totalling 280 km of
urban driving, a small portion of which we also use to learn the weights of the
modified architecture. As this work represents a novel application for FMCW
radar, we analyse the utility of the proposed method via a comprehensive set of
metrics which provide insight into the efficacy when used in a realistic
system, showing improved performance over the root architecture even in the
face of random rotational perturbation.Comment: submitted to the 2020 International Conference on Robotics and
Automation (ICRA
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE(2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
Visual Place Recognition is a challenging task for robotics and autonomous
systems, which must deal with the twin problems of appearance and viewpoint
change in an always changing world. This paper introduces Patch-NetVLAD, which
provides a novel formulation for combining the advantages of both local and
global descriptor methods by deriving patch-level features from NetVLAD
residuals. Unlike the fixed spatial neighborhood regime of existing local
keypoint features, our method enables aggregation and matching of deep-learned
local features defined over the feature-space grid. We further introduce a
multi-scale fusion of patch features that have complementary scales (i.e. patch
sizes) via an integral feature space and show that the fused features are
highly invariant to both condition (season, structure, and illumination) and
viewpoint (translation and rotation) changes. Patch-NetVLAD outperforms both
global and local feature descriptor-based methods with comparable compute,
achieving state-of-the-art visual place recognition results on a range of
challenging real-world datasets, including winning the Facebook Mapillary
Visual Place Recognition Challenge at ECCV2020. It is also adaptable to user
requirements, with a speed-optimised version operating over an order of
magnitude faster than the state-of-the-art. By combining superior performance
with improved computational efficiency in a configurable framework,
Patch-NetVLAD is well suited to enhance both stand-alone place recognition
capabilities and the overall performance of SLAM systems.Comment: Accepted to IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2021