Learning to See the Wood for the Trees: Deep Laser Localization in Urban and Natural Environments on a CPU

Localization in challenging, natural environments such as forests or woodlands is an important capability for many applications from guiding a robot navigating along a forest trail to monitoring vegetation growth with handheld sensors. In this work we explore laser-based localization in both urban and natural environments, which is suitable for online applications. We propose a deep learning approach capable of learning meaningful descriptors directly from 3D point clouds by comparing triplets (anchor, positive and negative examples). The approach learns a feature space representation for a set of segmented point clouds that are matched between a current and previous observations. Our learning method is tailored towards loop closure detection resulting in a small model which can be deployed using only a CPU. The proposed learning method would allow the full pipeline to run on robots with limited computational payload such as drones, quadrupeds or UGVs.Comment: Accepted for publication at RA-L/ICRA 2019. More info: https://ori.ox.ac.uk/esm-localizatio

Seeing the Wood for the Trees: Reliable Localization in Urban and Natural Environments

In this work we introduce Natural Segmentation and Matching (NSM), an algorithm for reliable localization, using laser, in both urban and natural environments. Current state-of-the-art global approaches do not generalize well to structure-poor vegetated areas such as forests or orchards. In these environments clutter and perceptual aliasing prevents repeatable extraction of distinctive landmarks between different test runs. In natural forests, tree trunks are not distinctive, foliage intertwines and there is a complete lack of planar structure. In this paper we propose a method for place recognition which uses a more involved feature extraction process which is better suited to this type of environment. First, a feature extraction module segments stable and reliable object-sized segments from a point cloud despite the presence of heavy clutter or tree foliage. Second, repeatable oriented key poses are extracted and matched with a reliable shape descriptor using a Random Forest to estimate the current sensor's position within the target map. We present qualitative and quantitative evaluation on three datasets from different environments - the KITTI benchmark, a parkland scene and a foliage-heavy forest. The experiments show how our approach can achieve place recognition in woodlands while also outperforming current state-of-the-art approaches in urban scenarios without specific tuning

Predicting Alignment Risk to Prevent Localization Failure

During localization and mapping the success of point cloud registration can be compromised when there is an absence of geometric features or constraints in corridors or across doorways, or when the volumes scanned only partly overlap, due to occlusions or constrictions between subsequent observations. This work proposes a strategy to predict and prevent laser-based localization failure. Our solution relies on explicit analysis of the point cloud content prior to registration. A model predicting the risk of a failed alignment is learned by analysing the degree of spatial overlap between two input point clouds and the geometric constraints available within the region of overlap. We define a novel measure of alignability for these constraints. The method is evaluated against three real-world datasets and compared to baseline approaches. The experiments demonstrate how our approach can help improve the reliability of laser-based localization during exploration of unknown and cluttered man-made environments

InstaLoc: One-shot Global Lidar Localisation in Indoor Environments through Instance Learning

Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctive objects and structures. Mimicking the human approach, InstaLoc identifies and matches object instances in the scene with those from a prior map. As far as we know, this is the first method to use panoptic segmentation directly inferring on 3D lidar scans for indoor localization. InstaLoc operates through two networks based on spatially sparse tensors to directly infer dense 3D lidar point clouds. The first network is a panoptic segmentation network that produces object instances and their semantic classes. The second smaller network produces a descriptor for each object instance. A consensus based matching algorithm then matches the instances to the prior map and estimates a six degrees of freedom (DoF) pose for the input cloud in the prior map. The significance of InstaLoc is that it has two efficient networks. It requires only one to two hours of training on a mobile GPU and runs in real-time at 1 Hz. Our method achieves between two and four times more detections when localizing, as compared to baseline methods, and achieves higher precision on these detections.Comment: This paper is presented at the Robotics: Science and Systems (RSS) 202

Balkan Vegetation Database: historical background, current status and future perspectives

The Balkan Vegetation Database (BVD; GIVD ID: EU-00-019; http://www.givd.info/ID/EU-00- 019) is a regional database that consists of phytosociological relevés from different vegetation types from six countries on the Balkan Peninsula (Albania, Bosnia and Herzegovina, Bulgaria, Kosovo, Montenegro and Serbia). Currently, it contains 9,580 relevés, and most of them (78%) are geo-referenced. The database includes digitized relevés from the literature (79%) and unpublished data (21%). Herein we present descriptive statistics about attributive relevé information. We developed rules that regulate governance of the database, data provision, types of data availability regimes, data requests and terms of use, authorships and relationships with other databases. The database offers an extensive overview about studies on the local, regional and SE European levels including information about flora, vegetation and habitats

Balkan Vegetation Database (BVD) – updated information and current status

The Balkan Vegetation Database (BVD; GIVD ID: EU-00-019) is a regional database, which was established in 2014. It comprises phytosociological relevés covering various vegetation types from nine countries of the Balkan Peninsula (Albania – 153 relevés, Bosnia and Herzegovina – 1715, Bulgaria – 12,282, Greece – 465, Croatia – 69, Kosovo – 493, Montenegro – 440, North Macedonia – 13 and Serbia – 2677). Currently, it contains 18,306 relevés (compared to 9.580 in 2016), and most of them (82.8%) are geo-referenced. The database includes both digitized relevés from the literature (65.6%) and unpublished data (34.5%). Plot size is available for 84.7% of all relevés. During the last four years some “header data information” was improved e.g. elevation (now available for 83.4% of all relevés), aspect (67.7%), slope (66%), total cover of vegetation (54.3%), cover of tree, shrub, herb, bryophyte and lichen layers (27.1%, 20.1%, 40.2%, 11.5% and 2.1%), respectively. Data access is either semi-restricted (65.6%) or restricted (34.4%). Most relevés (84.6%) are classified to syntaxa of different levels. The database has been used for numerous studies with various objectives from floristic, vegetation and habitat-related topics, to macroecological studies at the local, regional, national, continental and global levels. During the last four years, BVD data were requested from 111 different projects via the EVA and sPlot databases