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We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across...
In this paper we propose an on-line system that discovers and drives collision-free traversable paths, using a variational approach to dense stereo vision. Our system is light weight, can be run on low cost hardware and is remarkably quick to predict the semantics. In addition to the scene's path affordance it yields a segmentation of the local scene as a composite of distinctive labels - e.g, ground,...
In mobile robotics applications, generation of accurate static maps is encumbered by the presence of ephemeral objects such as vehicles, pedestrians, or bicycles. We propose a method to process a sequence of laser point clouds and back-fill dense surfaces into gaps caused by removing objects from the scene - a valuable tool in scenarios where resource constraints permit only one mapping pass in a...
This paper is about discovering and leveraging architectural constraints in large scale 3D reconstructions using laser. Our contribution is to offer a formulation of the problem which naturally and in a unified way, captures the variety of architectural constraints that can be discovered and applied in urban reconstructions. We focus in particular on the case of survey construction with a push broom...
We propose a calibration method that automatically estimates the extrinsic calibration between a sensor pose-graph from natural scenes. The sensor pose-graph represents a system of sensors comprising of lidars and cameras, without sensor co-visibility constraints. The method addresses the fact that each scene contributes differently to the calibration problem by introducing a diligent scene selection...
In this paper, we present a low cost localization system that exploits dense image information to continuously track the position of a camera in 6DOF. It leverages of the use of a set of selected "key frames" separated in distance from which a depth map is available to create a local 3D point cloud. In this way, we avoid the computational overload caused by common dense sequential approaches...
We propose an automatic, targetless, data-driven, extrinsic calibration method to calibrate push-broom 2D lidars with a multi-camera system. The calibration problem is decoupled into alternating optimisers over two hierarchical levels, where both levels are linked with a penalty term. The lower-level optimises the six degrees-of-freedom (DoF) rigid-body transforms between the lidar and each camera...
We present a framework for integrating two layers of map which are often required for fully automated operation: metric and semantic. Metric maps are likely to improve with subsequent visitations to the same place, while semantic maps can comprise both permanent and fluctuating features of the environment. However, it is not clear from the state of the art how to update the semantic layer as the metric...
This paper is about dense depthmap estimation using a monocular camera in workspaces with extensive textureless surfaces. Current state of the art techniques have been shown to work in real time with an admirable performance in desktop-size environments. Unfortunately, as we show in this paper, when applied to larger indoor environments, performance often degrades. A common cause is the presence of...
In this paper we address the problem of dense depth map estimation from sparse noisy range data to reconstruct large heterogeneous outdoor scenes. We propose a surface inpainting solution through energy minimisation with an adaptive selection of surface regularisers among a set of well known convex and non-convex regularisers. In fact, the selection of norm is pivotal with respect to the intrinsic...
In this paper we present an online approach to segmenting roads on large scale trajectories using only a monocular camera mounted on a car. We differ from popular 2D segmentation solutions which use single colour images and machine learning algorithms that require supervised training on huge image databases. Instead, we propose a novel approach that fuses 3D geometric data with appearance-based segmentation...
One of the most important properties that a robot must have in order to be considered autonomous is the ability to localize by itself in an unknown environment, using the information gathered by its sensors. The system uses a cheap web camera, carried by a mobile robot or by a person, while it builds a map and at the same time estimates its localization with respect to this map. To develop such system,...
Submapping and graphical methods have been shown to be valuable approaches to simultaneous localization and mapping (SLAM), providing significant advantages over the classical extended Kalman filter (EKF) solution: they are faster and, when using local coordinates, produce more consistent estimates. The main contribution of this paper is CI‐Graph SLAM, a novel algorithm that is able to efficiently...
When solving the Simultaneous Localization and Mapping (SLAM) problem, submapping and graphical methods have shown to be valuable approaches that provide significant advantages over the standard EKF solution: they are faster and can produce more consistent estimates when using local coordinates. In this paper we present CI-Graph, a submapping method for SLAM that uses a graph structure to efficiently...
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