3D scene graphs have recently emerged as a powerful and human-understandable way of representing complex 3D environments. These describe environments through a layered or hierarchical graph where nodes represent different spatial concepts (from low-level geometry to higher-level scene-scale reasoning) and the edges between them represent relationships. While these representations have shown great promise in indoor well-structured environments, their use in outdoor structured environments such as agricultural environments has been under-explored. A key challenge here is that concepts and structures often observed in urban indoor environments cannot be easily transferred to these novel scenes.Motivated by this challenge, this paper presents Osiris which is a 3D scene graph builder for agricultural environments. We first propose a structure of the hierarchical graph for agricultural environments consisting of rowed crops and through our proposed system Osiris incrementally construct a 3D scene graph of agricultural environments from data taken onboard a mobile robot. We validate and evaluate the performance of Osiris using real-world data collected at several farms and show that this system is able to accurately get to the underlying structure of these agricultural environments while presenting a metrically accurate and human-understandable representation.

A 3D mesh offers a rich yet lightweight representation of geometry and topology for the metric and semantic understanding of a robot’s scene. Noisy features are often used to generate the mesh which furthers the need for accurate regularisation. Current approaches tightly couple front-end optimisation with regularisation making it difficult to evaluate the choice of discretisation and regularisation on mesh accuracy. In this work, we aim to explicitly query the performance of a set of well-known convex and non-convex regularisers on the mesh optimisation problem. We then apply these norms to dense depth estimation from a mesh representation and evaluate their performance in indoor and outdoor environments.While we show that the use of exotic, non-convex regularisers such as logTV and logTGV can result in more faithful structural reconstruction under noise, this comes at the cost of stronger outlier persistence that limits their performance when compared to their convex equivalents. This represents a significant departure from results achieved when the same regularisers are applied in denser "every-pixel" scenarios and suggests that current discretisation techniques adopted for this problem are more sensitive to triangulation. This sensitivity is often obscured in many practical robotic applications by a rigorous front-end that removes artefacts from the mesh to be optimised. By decoupling the front and back-ends we therefore show that further consideration must be taken to align current mesh discretisations with the classical definitions of variational regularisers to allow the full benefit of their well-documented properties to be realised.

We propose a novel method for fitting multiple geometric models to multi-structural data via convex relaxation. Unlike greedy methods - which maximise the number of inliers - our approach efficiently searches for a soft assignment of points to geometric models by minimising the energy of the overall assignment. The inherently parallel nature of our approach, as compared to the sequential approach found in state-of-the-art energy minimisation techniques, allows for the elegant treatment of a scaling factor that occurs as the number of features in the data increases. This results in an energy minimisation that, per iteration, is as much as two orders of magnitude faster on comparable architectures thus bringing real-time, robust performance to a wider set of geometric multi-model fitting problems. We demonstrate the versatility of our approach on two canonical problems in estimating structure from images: plane extraction from RGB-D images and homography estimation from pairs of images. Our approach seamlessly adapts to the different metrics brought forth in these distinct problems. In both cases, we report results on publicly available data-sets that in most instances outperform the state-of-the-art while simultaneously presenting run-times that are as much as an order of magnitude faster.

Depth map estimation techniques from cameras often struggle to accurately estimate the depth of large textureless regions. In this work we present a vision-only method that accurately extracts planar priors from a viewed scene without making any assumptions of the underlying scene layout. Through a fast global labelling, these planar priors can be associated to the individual pixels leading to more complete depth-maps specifically over large, plain and planar regions that tend to dominate the urban environment. When these depth-maps are deployed to the creation of a vision only dense reconstruction over large scales, we demonstrate reconstructions that yield significantly better results in terms of coverage while still maintaining high accuracy.

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 laser + VO system. Here visual odometry is combined with vertical 2D scans to create a 3D picture of the environment. A key characteristic here is that the sensors pass/sweep swiftly through the environment such that elements of the scene are seen only briefly by cameras and scanned just once by the laser. These qualities make for a an ill-constrained optimisation problem which is greatly aided if architectural constraints can be discovered and appropriately applied. We demonstrate our approach in an end-to-end implementation which discovers salient architectural constraints and rejects false loop closures before invoking an optimisation to return a 3D model of the workspace. We evaluate the precision of this model by comparison to a ground truth provided by a 3rd party professional survey using highend (static) 3D laser scanners.