79 research outputs found
Active Robot Vision for Distant Object Change Detection: A Lightweight Training Simulator Inspired by Multi-Armed Bandits
In ground-view object change detection, the recently emerging mapless
navigation has great potential to navigate a robot to objects distantly
detected (e.g., books, cups, clothes) and acquire high-resolution object
images, to identify their change states (no-change/appear/disappear). However,
naively performing full journeys for every distant object requires huge
sense/plan/action costs, proportional to the number of objects and the
robot-to-object distance. To address this issue, we explore a new map-based
active vision problem in this work: ``Which journey should the robot select
next?" However, the feasibility of the active vision framework remains unclear;
Since distant objects are only uncertainly recognized, it is unclear whether
they can provide sufficient cues for action planning. This work presents an
efficient simulator for feasibility testing, to accelerate the early-stage R&D
cycles (e.g., prototyping, training, testing, and evaluation). The proposed
simulator is designed to identify the degree of difficulty that a robot vision
system (sensors/recognizers/planners/actuators) would face when applied to a
given environment (workspace/objects). Notably, it requires only one real-world
journey experience per distant object to function, making it suitable for an
efficient R&D cycle. Another contribution of this work is to present a new
lightweight planner inspired by the traditional multi-armed bandit problem.
Specifically, we build a lightweight map-based planner on top of the mapless
planner, which constitutes a hierarchical action planner. We verified the
effectiveness of the proposed framework using a semantically non-trivial
scenario ``sofa as bookshelf".Comment: 7 pages, 7 figures, technical repor
A Comprehensive Review on Autonomous Navigation
The field of autonomous mobile robots has undergone dramatic advancements
over the past decades. Despite achieving important milestones, several
challenges are yet to be addressed. Aggregating the achievements of the robotic
community as survey papers is vital to keep the track of current
state-of-the-art and the challenges that must be tackled in the future. This
paper tries to provide a comprehensive review of autonomous mobile robots
covering topics such as sensor types, mobile robot platforms, simulation tools,
path planning and following, sensor fusion methods, obstacle avoidance, and
SLAM. The urge to present a survey paper is twofold. First, autonomous
navigation field evolves fast so writing survey papers regularly is crucial to
keep the research community well-aware of the current status of this field.
Second, deep learning methods have revolutionized many fields including
autonomous navigation. Therefore, it is necessary to give an appropriate
treatment of the role of deep learning in autonomous navigation as well which
is covered in this paper. Future works and research gaps will also be
discussed
RH-Map: Online Map Construction Framework of Dynamic Objects Removal Based on Region-wise Hash Map Structure
Mobile robots navigating in outdoor environments frequently encounter the
issue of undesired traces left by dynamic objects and manifested as obstacles
on map, impeding robots from achieving accurate localization and effective
navigation. To tackle the problem, a novel map construction framework based on
3D region-wise hash map structure (RH-Map) is proposed, consisting of front-end
scan fresher and back-end removal modules, which realizes real-time map
construction and online dynamic object removal (DOR). First, a two-layer 3D
region-wise hash map structure of map management is proposed for effective
online DOR. Then, in scan fresher, region-wise ground plane estimation (R-GPE)
is adopted for estimating and preserving ground information and Scan-to-Map
Removal (S2M-R) is proposed to discriminate and remove dynamic regions.
Moreover, the lightweight back-end removal module maintaining keyframes is
proposed for further DOR. As experimentally verified on SemanticKITTI, our
proposed framework yields promising performance on online DOR of map
construction compared with the state-of-the-art methods. And we also validate
the proposed framework in real-world environments
Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments
Real-time detection of moving objects is an essential capability for robots
acting autonomously in dynamic environments. We thus propose Dynablox, a novel
online mapping-based approach for robust moving object detection in complex
environments. The central idea of our approach is to incrementally estimate
high confidence free-space areas by modeling and accounting for sensing, state
estimation, and mapping limitations during online robot operation. The
spatio-temporally conservative free space estimate enables robust detection of
moving objects without making any assumptions on the appearance of objects or
environments. This allows deployment in complex scenes such as multi-storied
buildings or staircases, and for diverse moving objects such as people carrying
various items, doors swinging or even balls rolling around. We thoroughly
evaluate our approach on real-world data sets, achieving 86% IoU at 17 FPS in
typical robotic settings. The method outperforms a recent appearance-based
classifier and approaches the performance of offline methods. We demonstrate
its generality on a novel data set with rare moving objects in complex
environments. We make our efficient implementation and the novel data set
available as open-source.Comment: Code released at https://github.com/ethz-asl/dynablo
A review of UAV autonomous navigation in GPS-denied environments
Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years, leading to a vast number of applications, such as, terrain exploration, disaster assistance and industrial inspection. Unlike UAV navigation in outdoor environments that rely on GPS (Global Positioning System) for localization, indoor navigation cannot rely on GPS due to the poor quality or lack of signal. Although some reviewing papers particularly summarized indoor navigation strategies (e.g., Visual-based Navigation) or their specific sub-components (e.g., localization and path planning) in detail, there still lacks a comprehensive survey for the complete navigation strategies that cover different technologies. This paper proposes a taxonomy which firstly classifies the navigation strategies into Mapless and Map-based ones based on map usage and then, respectively categorizes the Mapless navigation into Integrated, Direct and Indirect approaches via common characteristics. The Map-based navigation is then split into Known Map/Spaces and Map-building via prior knowledge. In order to analyze these navigation strategies, this paper uses three evaluation metrics (Path Length, Deviation Rate and Exploration Efficiency) according to the common purposes of navigation to show how well they can perform. Furthermore, three representative strategies were selected and 120 flying experiments conducted in two reality-like simulated indoor environments to show their performances against the evaluation metrics proposed in this paper, i.e., the ratio of Successful Flight, the Mean time of Successful Flight, the Mean Length of Successful Flight, the Mean time of Flight, and the Mean Length of Flight. In comparison to the CNN-based Supervised Learning (directly maps visual observations to UAV controls) and the Frontier-based navigation (necessitates continuous global map generation), the experiments show that the CNN-based Distance Estimation for navigation trades off the ratio of Successful Flight and the required time and path length. Moreover, this paper identifies the current challenges and opportunities which will drive UAV navigation research in GPS-denied environments
- …