This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that Vote3Deep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.

This paper proposes an appearance-based approach to estimating localisation performance in the context of visual teach and repeat. Specifically, it aims to estimate the likely corridor around a taught trajectory within which a vision-based localisation system is still able to localise itself. In contrast to prior art, our system is able to predict this localisation envelope for trajectories in similar, yet geographically distant locations where no repeat runs have yet been performed. Thus, by characterising the localisation performance in one region, we are able to predict performance in another. To achieve this, we leverage a Gaussian Process regressor to estimate the likely number of feature matches for any keyframe in the teach run, based on a combination of trajectory properties such as curvature and an appearance model of the keyframe. Using data from real traversals, we demonstrate that our approach performs as well as prior art when it comes to interpolating localisation performance based on a number of repeat runs, while also performing well at generalising performance estimation to freshly taught trajectories.

This paper is about building maps which not only contain the traditional information useful for localising — such as point features — but also embeds a spatial model of expected localiser performance. This often overlooked second-order information provides vital context when it comes to map use and planning. Our motivation here is to improve the performance of the popular Teach and Repeat paradigm [1] which has been shown to enable truly large-scale field operation. When using the taught route for localisation, it is often assumed the robot is following exactly, or is sufficiently close to, the original path, enabling successful localisation. However, what happens if it is not possible, or not desirable to exactly follow the mapped path? How far off the beaten track can the robot travel before it gets lost? We present an approach for assessing this localisation area around a taught route, which we refer to as the localisation envelope. Using a combination of physical sampling and a Gaussian Process model, we are able to accurately predict the localisation performance at unseen points.

This paper explores the idea of reducing a robot's energy consumption while following a trajectory by turning off the main localisation subsystem and switching to a lower-powered, less accurate odometry source at appropriate times. This applies to scenarios where the robot is permitted to deviate from the original trajectory, which allows for energy savings. Sensor scheduling is formulated as a probabilistic belief planning problem. Two algorithms are presented which generate feasible perception schedules: the first is based upon a simple heuristic; the second leverages dynamic programming to obtain optimal plans. Both simulations and real-world experiments on a planetary rover prototype demonstrate over 50% savings in perception-related energy, which translates into a 12% reduction in total energy consumption.

In this paper, we present a method for obtaining Visual Odometry (VO) estimates using a scanning laser rangefinder. Though common VO implementations utilize stereo camera imagery, cameras are dependent on ambient light. In contrast, actively-illuminated sensors such as laser rangefinders work in a variety of lighting conditions, including full darkness. We leverage previous successes by applying sparse appearance-based methods to laser intensity images, and address the issue of motion distortion by considering the estimation problem in continuous time. This is facilitated by Gaussian Process Gauss-Newton (GPGN), an algorithm for non-parametric, continuous-time, nonlinear, batch state estimation. We include a concise derivation of GPGN, along with details on the extension to three-dimensions (3D). Validation of the 3D laser-based VO framework is provided using 1.1km of experimental data, which was gathered by a field robot equipped with a two-axis scanning lidar.

In this paper, the problem of outliers in a batch alignment problem (given heterogeneous measurements and sparse features) is considered. The conventional approach from the field of computer vision, pairwise RANSAC, is shown to be inappropriate for this scenario, which motivates the need for a new method. To address this problem, the heterogeneous measurements are compared in a common currency using their respective scaled measurement innovations. Furthermore, a family of three algorithms for classifying outliers given a hypothesis model are presented, each having its own balance between speed and accuracy. These classification criteria are then incorporated through iterative reclassification in a batch alignment framework, providing a robust estimate for localization and mapping. Lastly, statistical validation is obtained through a large set of simulated trials.

In this paper, we present an infrastructure-based ground-truth localization system suitable for deployment in large worksite environments. In particular, the system is low cost, simple-to-deploy, and is able to provide full six-degree of-freedom relative localization for three-dimensional laser scanners with centimetre-level accuracy in translation, and half degree accuracy in orientation. This system utilizes common laser scanner hardware, and exploits the fact that retroreflective material is easily identified based on the return intensity. This enables the use of simple rectangular signs placed around the scene as landmarks. An uncertainty model is presented that accounts for the shape of the landmarks, and a batch alignment algorithm is formulated that efficiently considers the structure of the problem. Lastly, characterization of the accuracy of the system is provided through small-scale testing in an indoor lab, and examples for a large-scale setup.

In this paper, we present a robust framework suitable for conducting three-dimensional Simultaneous Localization and Mapping (3D SLAM) in a planetary worksite environment. By utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse-feature-based approach, and conducts data association using a hybrid combination of feature constellations and dense data. To maintain global consistency, the measurements are resolved using a batch alignment algorithm, which is reinforced with batch outlier rejection to improve its robustness. Finally, a map is created from the alignment estimates and the dense data. Validation is provided using data gathered at two different planetary analogue facilities.