| 1. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
- 2019
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Ingår i: 2019 European Conference on Mobile Robots (ECMR). - : IEEE. - 9781728136059
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Konferensbidrag (refereegranskat)abstract
- This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.
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| 2. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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Improving Localisation Accuracy using Submaps in warehouses
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a method for localisation in hybrid metric-topological maps built using only local information that is, only measurements that were captured by the robot when it was in a nearby location. The motivation is that observations are typically range and viewpoint dependent and that a map a discrete map representation might not be able to explain the full structure within a voxel. The localisation system uses a method to select submap based on how frequently and where from each submap was updated. This allow the system to select the most descriptive submap, thereby improving the localisation and increasing performance by up to 40%.
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| 3. |
- Adolfsson, Daniel, 1992-
(författare)
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Robust large-scale mapping and localization : Combining robust sensing and introspection
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.
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| 4. |
- Andreasson, Henrik, 1977-, et al.
(författare)
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A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:7
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.
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| 5. |
- Chen, Zetao, et al.
(författare)
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Bio-inspired homogeneous multi-scale place recognition
- 2015
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Ingår i: Neural Networks. - : Elsevier. - 0893-6080 .- 1879-2782. ; 72, s. 48-61
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Tidskriftsartikel (refereegranskat)abstract
- Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.
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| 6. |
- Chen, Zetao, et al.
(författare)
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Distance metric learning for feature-agnostic place recognition
- 2015
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Ingår i: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. ; , s. 2556-2563
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Konferensbidrag (refereegranskat)abstract
- The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.
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| 7. |
- Kucner, Tomasz Piotr, PhD, 1988-, et al.
(författare)
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Robust Frequency-Based Structure Extraction
- 2021
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Ingår i: 2021 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9781728190778 - 9781728190785 ; , s. 1715-1721
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Konferensbidrag (refereegranskat)abstract
- State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.
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| 8. |
- Kurtser, Polina, 1990-, et al.
(författare)
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RGB-D datasets for robotic perception in site-specific agricultural operations : a survey
- 2023
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Ingår i: Computers and Electronics in Agriculture. - : Elsevier. - 0168-1699 .- 1872-7107. ; 212
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Tidskriftsartikel (refereegranskat)abstract
- Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.
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| 9. |
- Lowry, Stephanie, 1979-, et al.
(författare)
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Building Beliefs : Unsupervised Generation of Observation Likelihoods for Probabilistic Localization in Changing Environments
- 2015
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Ingår i: IEEE International Conference on Intelligent Robots and Systems (IROS), IEEE, 2015. - New York, USA : IEEE. - 9781479999941 ; , s. 3071-3078
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Konferensbidrag (refereegranskat)abstract
- This paper is concerned with the interpretation of visual information for robot localization. It presents a probabilistic localization system that generates an appropriate observation model online, unlike existing systems which require pre-determined belief models. This paper proposes that probabilistic visual localization requires two major operating modes - one to match locations under similar conditions and the other to match locations under different conditions. We develop dual observation likelihood models to suit these two different states, along with a similarity measure-based method that identifies the current conditions and switches between the models. The system is experimentally tested against different types of ongoing appearance change. The results demonstrate that the system is compatible with a wide range of visual front-ends, and the dual-model system outperforms a single-model or pre-trained approach and state-of-the-art localization techniques.
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| 10. |
- Lowry, Stephanie, 1979-, et al.
(författare)
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Lightweight, Viewpoint-Invariant Visual Place Recognition in Changing Environments
- 2018
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 3:2, s. 957-964
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.
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