MRG Highlights

/MRG Highlights

A category to choose which research topics / news items should show up on the front page.

Introspective Radar Odometry

How do we know when we don’t know? This is an important question to answer in any situation where we need to navigate through our surroundings, and something any autonomous mobile robot needs to know too. We discuss this introspection capability and its importance to our radar-based navigation algorithms [...]

Introspective Radar Odometry2019-07-24T09:20:24+01:00

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Accurate scene understanding is paramount to the deployment of autonomous vehicles in real-world traffic. They need to perceive and fully understand their environment to accomplish their navigation tasks in a natural and safe manner. To accomplish this, we have recently introduced a hierarchical framework to describe [...]

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping2019-06-24T14:22:58+01:00

Fast Radar Motion Estimation

Fast Radar Motion Estimation This blog post provides an overview of our paper “Fast Radar Motion Estimation with a Learnt Focus of Attention using Weak Supervision” by Roberto Aldera, Daniele De Martini, Matthew Gadd, and Paul Newman which was recently accepted for publication at the IEEE International Conference on [...]

Fast Radar Motion Estimation2019-07-30T14:53:37+01:00

Road Boundary Detection

This blog post provides an overview of our paper “Inferring Road Boundaries Through and Despite Traffic” by Tarlan Suleymanov, Paul Amayo and Paul Newman, which has been accepted for publication at the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) 2018. In the context of autonomous driving, road [...]

Road Boundary Detection2019-05-15T13:54:20+01:00

Radar for mobile autonomy

Classic sensor systems for mobile robotic platforms depend primarily on vision and lidar sensors, incorporating the GPS and IMU for additional robustness. Although radar has been well studied for target detection over the past century, its role in mobile robotics has been limited. Radars have mostly served as warning sensors, alerting autonomous systems [...]

Radar for mobile autonomy2019-05-15T13:53:09+01:00

Geometric Multi-Model Extraction For Robotics

The extraction of geometric models has long been of interest to the robotics community. Many interesting applications such as homography,plane estimation and ego-motion estimation demand the ability to fit geometric models onto noisy data. Additionally, these geometric models are often drivers of other algorithms for algorithms  navigation, perception and 3D reconstructions creating an increased [...]

Geometric Multi-Model Extraction For Robotics2019-05-15T13:55:37+01:00

Dense Reconstruction Dataset   In practice, we found dense reconstructions are the most complete and highest quality (with our mobile robotics platform) when fusing data from multiple Velodyne, SICK LMS- 151, and stereo cameras using our own datasets. We are releasing such a dataset to provide a realistic mobile-robotics platform with a variety of sensors—a [...]

Dense Reconstruction Dataset2019-05-13T15:44:30+01:00

Generation and Exploitation of Local Orthographic Imagery for Road Vehicle Localisation

    This work performs visual localisation using synthesised local orthographic imagery. We exploit state of the art stereo visual odometry (VO) on our survey vehicle to generate high precision synthetic orthographic images of the road surface as would be seen from overhead. The fidelity and detail of these images far exceeds that of aerial photographs. When [...]

Generation and Exploitation of Local Orthographic Imagery for Road Vehicle Localisation2019-08-03T14:15:06+01:00

Planning Most-Likely Paths from Overhead Imagery

In this work, we are concerned with planning paths from overhead imagery. The novelty here lies in taking explicit account of uncertainty in terrain classification and spatial variation in terrain cost. The image is first classified using a multi-class Gaussian Process Classifier which provides probabilities of class membership at each location in the image, which is then combined [...]

Planning Most-Likely Paths from Overhead Imagery2019-08-03T14:16:05+01:00