13 research outputs found
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been
presented in last decade using different sensor modalities. However, robust
SLAM in extreme weather conditions is still an open research problem. In this
paper, RadarSLAM, a full radar based graph SLAM system, is proposed for
reliable localization and mapping in large-scale environments. It is composed
of pose tracking, local mapping, loop closure detection and pose graph
optimization, enhanced by novel feature matching and probabilistic point cloud
generation on radar images. Extensive experiments are conducted on a public
radar dataset and several self-collected radar sequences, demonstrating the
state-of-the-art reliability and localization accuracy in various adverse
weather conditions, such as dark night, dense fog and heavy snowfall
Radar-on-Lidar: metric radar localization on prior lidar maps
Radar and lidar, provided by two different range sensors, each has pros and
cons of various perception tasks on mobile robots or autonomous driving. In
this paper, a Monte Carlo system is used to localize the robot with a rotating
radar sensor on 2D lidar maps. We first train a conditional generative
adversarial network to transfer raw radar data to lidar data, and achieve
reliable radar points from generator. Then an efficient radar odometry is
included in the Monte Carlo system. Combining the initial guess from odometry,
a measurement model is proposed to match the radar data and prior lidar maps
for final 2D positioning. We demonstrate the effectiveness of the proposed
localization framework on the public multi-session dataset. The experimental
results show that our system can achieve high accuracy for long-term
localization in outdoor scenes
LiDAR Lateral Localisation Despite Challenging Occlusion from Traffic
This paper presents a system for improving the robustness of LiDAR lateral
localisation systems. This is made possible by including detections of road
boundaries which are invisible to the sensor (due to occlusion, e.g. traffic)
but can be located by our Occluded Road Boundary Inference Deep Neural Network.
We show an example application in which fusion of a camera stream is used to
initialise the lateral localisation. We demonstrate over four driven forays
through central Oxford - totalling 40 km of driving - a gain in performance
that inferring of occluded road boundaries brings.Comment: accepted for publication at the IEEE/ION Position, Location and
Navigation Symposium (PLANS) 202
Keep off the Grass: Permissible Driving Routes from Radar with Weak Audio Supervision
Reliable outdoor deployment of mobile robots requires the robust
identification of permissible driving routes in a given environment. The
performance of LiDAR and vision-based perception systems deteriorates
significantly if certain environmental factors are present e.g. rain, fog,
darkness. Perception systems based on FMCW scanning radar maintain full
performance regardless of environmental conditions and with a longer range than
alternative sensors. Learning to segment a radar scan based on driveability in
a fully supervised manner is not feasible as labelling each radar scan on a
bin-by-bin basis is both difficult and time-consuming to do by hand. We
therefore weakly supervise the training of the radar-based classifier through
an audio-based classifier that is able to predict the terrain type underneath
the robot. By combining odometry, GPS and the terrain labels from the audio
classifier, we are able to construct a terrain labelled trajectory of the robot
in the environment which is then used to label the radar scans. Using a
curriculum learning procedure, we then train a radar segmentation network to
generalise beyond the initial labelling and to detect all permissible driving
routes in the environment.Comment: accepted for publication at the IEEE Intelligent Transportation
Systems Conference (ITSC) 202
Sense-Assess-eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios
This paper discusses ongoing work in demonstrating research in mobile
autonomy in challenging driving scenarios. In our approach, we address
fundamental technical issues to overcome critical barriers to assurance and
regulation for large-scale deployments of autonomous systems. To this end, we
present how we build robots that (1) can robustly sense and interpret their
environment using traditional as well as unconventional sensors; (2) can assess
their own capabilities; and (3), vitally in the purpose of assurance and trust,
can provide causal explanations of their interpretations and assessments. As it
is essential that robots are safe and trusted, we design, develop, and
demonstrate fundamental technologies in real-world applications to overcome
critical barriers which impede the current deployment of robots in economically
and socially important areas. Finally, we describe ongoing work in the
collection of an unusual, rare, and highly valuable dataset.Comment: accepted for publication at the IEEE Intelligent Vehicles Symposium
(IV), Workshop on Ensuring and Validating Safety for Automated Vehicles
(EVSAV), 2020, project URL:
https://ori.ox.ac.uk/projects/sense-assess-explain-sa
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar
This paper presents an efficient annotation procedure and an application
thereof to end-to-end, rich semantic segmentation of the sensed environment
using FMCW scanning radar. We advocate radar over the traditional sensors used
for this task as it operates at longer ranges and is substantially more robust
to adverse weather and illumination conditions. We avoid laborious manual
labelling by exploiting the largest radar-focused urban autonomy dataset
collected to date, correlating radar scans with RGB cameras and LiDAR sensors,
for which semantic segmentation is an already consolidated procedure. The
training procedure leverages a state-of-the-art natural image segmentation
system which is publicly available and as such, in contrast to previous
approaches, allows for the production of copious labels for the radar stream by
incorporating four camera and two LiDAR streams. Additionally, the losses are
computed taking into account labels to the radar sensor horizon by accumulating
LiDAR returns along a pose-chain ahead and behind of the current vehicle
position. Finally, we present the network with multi-channel radar scan inputs
in order to deal with ephemeral and dynamic scene objects.Comment: submitted to IEEE Intelligent Vehicles Symposium (IV) 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
Look Around You: Sequence-based Radar Place Recognition with Learned Rotational Invariance
This paper details an application which yields significant improvements to
the adeptness of place recognition with Frequency-Modulated Continuous-Wave
radar - a commercially promising sensor poised for exploitation in mobile
autonomy. We show how a rotationally-invariant metric embedding for radar scans
can be integrated into sequence-based trajectory matching systems typically
applied to videos taken by visual sensors. Due to the complete horizontal field
of view inherent to the radar scan formation process, we show how this
off-the-shelf sequence-based trajectory matching system can be manipulated to
detect place matches when the vehicle is travelling down a previously visited
stretch of road in the opposite direction. We demonstrate the efficacy of the
approach on 26 km of challenging urban driving taken from the largest
radar-focused urban autonomy dataset released to date -- showing a boost of 30%
in recall at high levels of precision over a nearest neighbour approach.Comment: accepted for publication at the IEEE/ION Position, Location and
Navigation Symposium (PLANS) 202