16 research outputs found
EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Association
Object-level data association and pose estimation play a fundamental role in
semantic SLAM, which remain unsolved due to the lack of robust and accurate
algorithms. In this work, we propose an ensemble data associate strategy for
integrating the parametric and nonparametric statistic tests. By exploiting the
nature of different statistics, our method can effectively aggregate the
information of different measurements, and thus significantly improve the
robustness and accuracy of data association. We then present an accurate object
pose estimation framework, in which an outliers-robust centroid and scale
estimation algorithm and an object pose initialization algorithm are developed
to help improve the optimality of pose estimation results. Furthermore, we
build a SLAM system that can generate semi-dense or lightweight object-oriented
maps with a monocular camera. Extensive experiments are conducted on three
publicly available datasets and a real scenario. The results show that our
approach significantly outperforms state-of-the-art techniques in accuracy and
robustness. The source code is available on:
https://github.com/yanmin-wu/EAO-SLAM.Comment: Accepted to IROS 2020. Project Page:
https://yanmin-wu.github.io/project/eaoslam/; Code:
https://github.com/yanmin-wu/EAO-SLA
CAPRICORN: Communication Aware Place Recognition using Interpretable Constellations of Objects in Robot Networks
Using multiple robots for exploring and mapping environments can provide
improved robustness and performance, but it can be difficult to implement. In
particular, limited communication bandwidth is a considerable constraint when a
robot needs to determine if it has visited a location that was previously
explored by another robot, as it requires for robots to share descriptions of
places they have visited. One way to compress this description is to use
constellations, groups of 3D points that correspond to the estimate of a set of
relative object positions. Constellations maintain the same pattern from
different viewpoints and can be robust to illumination changes or dynamic
elements. We present a method to extract from these constellations compact
spatial and semantic descriptors of the objects in a scene. We use this
representation in a 2-step decentralized loop closure verification: first, we
distribute the compact semantic descriptors to determine which other robots
might have seen scenes with similar objects; then we query matching robots with
the full constellation to validate the match using geometric information. The
proposed method requires less memory, is more interpretable than global image
descriptors, and could be useful for other tasks and interactions with the
environment. We validate our system's performance on a TUM RGB-D SLAM sequence
and show its benefits in terms of bandwidth requirements.Comment: 8 pages, 6 figures, 1 table. 2020 IEEE International Conference on
Robotics and Automation (ICRA
Robust Estimation Framework with Semantic Measurements
Conventional simultaneous localization and mapping (SLAM) algorithms rely on geometric measurements and require loop-closure detections to correct for drift accumulated over a vehicle trajectory. Semantic measurements can add measurement redundancy and provide an alternative form of loop closure. We propose two different estimation algorithms that incorporate semantic measurements provided by vision-based object classifiers. An a priori map of regions where the objects can be detected is assumed. The first estimation framework is posed as a maximum-likelihood problem, where the likelihood function for semantic measurements is derived from the confusion matrices of the object classifiers. The second estimation framework is comprised of two parts: 1) a continuous-state estimation formulation that includes semantic measurements as a form of state constraints and 2) a discrete-state estimation formulation used to compute the certainty of object detection measurements using a Hidden Markov Model (HMM). The advantages of incorporating semantic measurements in these frameworks are demonstrated in numerical simulations. In particular, the proposed estimation algorithms improve upon the robustness and accuracy of conventional SLAM algorithms
Robust Estimation Framework with Semantic Measurements
Conventional simultaneous localization and mapping (SLAM) algorithms rely on geometric measurements and require loop-closure detections to correct for drift accumulated over a vehicle trajectory. Semantic measurements can add measurement redundancy and provide an alternative form of loop closure. We propose two different estimation algorithms that incorporate semantic measurements provided by vision-based object classifiers. An a priori map of regions where the objects can be detected is assumed. The first estimation framework is posed as a maximum-likelihood problem, where the likelihood function for semantic measurements is derived from the confusion matrices of the object classifiers. The second estimation framework is comprised of two parts: 1) a continuous-state estimation formulation that includes semantic measurements as a form of state constraints and 2) a discrete-state estimation formulation used to compute the certainty of object detection measurements using a Hidden Markov Model (HMM). The advantages of incorporating semantic measurements in these frameworks are demonstrated in numerical simulations. In particular, the proposed estimation algorithms improve upon the robustness and accuracy of conventional SLAM algorithms
An Object SLAM Framework for Association, Mapping, and High-Level Tasks
Object SLAM is considered increasingly significant for robot high-level
perception and decision-making. Existing studies fall short in terms of data
association, object representation, and semantic mapping and frequently rely on
additional assumptions, limiting their performance. In this paper, we present a
comprehensive object SLAM framework that focuses on object-based perception and
object-oriented robot tasks. First, we propose an ensemble data association
approach for associating objects in complicated conditions by incorporating
parametric and nonparametric statistic testing. In addition, we suggest an
outlier-robust centroid and scale estimation algorithm for modeling objects
based on the iForest and line alignment. Then a lightweight and object-oriented
map is represented by estimated general object models. Taking into
consideration the semantic invariance of objects, we convert the object map to
a topological map to provide semantic descriptors to enable multi-map matching.
Finally, we suggest an object-driven active exploration strategy to achieve
autonomous mapping in the grasping scenario. A range of public datasets and
real-world results in mapping, augmented reality, scene matching,
relocalization, and robotic manipulation have been used to evaluate the
proposed object SLAM framework for its efficient performance.Comment: Accepted by IEEE Transactions on Robotics(T-RO
Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
Motivated by the tremendous progress we witnessed in recent years, this paper
presents a survey of the scientific literature on the topic of Collaborative
Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM.
With fleets of self-driving cars on the horizon and the rise of multi-robot
systems in industrial applications, we believe that Collaborative SLAM will
soon become a cornerstone of future robotic applications. In this survey, we
introduce the basic concepts of C-SLAM and present a thorough literature
review. We also outline the major challenges and limitations of C-SLAM in terms
of robustness, communication, and resource management. We conclude by exploring
the area's current trends and promising research avenues.Comment: 44 pages, 3 figure
WSR: A WiFi Sensor for Collaborative Robotics
In this paper we derive a new capability for robots to measure relative
direction, or Angle-of-Arrival (AOA), to other robots operating in
non-line-of-sight and unmapped environments with occlusions, without requiring
external infrastructure. We do so by capturing all of the paths that a WiFi
signal traverses as it travels from a transmitting to a receiving robot, which
we term an AOA profile. The key intuition is to "emulate antenna arrays in the
air" as the robots move in 3D space, a method akin to Synthetic Aperture Radar
(SAR). The main contributions include development of i) a framework to
accommodate arbitrary 3D trajectories, as well as continuous mobility all
robots, while computing AOA profiles and ii) an accompanying analysis that
provides a lower bound on variance of AOA estimation as a function of robot
trajectory geometry based on the Cramer Rao Bound. This is a critical
distinction with previous work on SAR that restricts robot mobility to
prescribed motion patterns, does not generalize to 3D space, and/or requires
transmitting robots to be static during data acquisition periods. Our method
results in more accurate AOA profiles and thus better AOA estimation, and
formally characterizes this observation as the informativeness of the
trajectory; a computable quantity for which we derive a closed form. All
theoretical developments are substantiated by extensive simulation and hardware
experiments. We also show that our formulation can be used with an
off-the-shelf trajectory estimation sensor. Finally, we demonstrate the
performance of our system on a multi-robot dynamic rendezvous task.Comment: 28 pages, 25 figures, *co-primary author
Assessment of simulated and real-world autonomy performance with small-scale unmanned ground vehicles
Off-road autonomy is a challenging topic that requires robust systems to both understand and navigate complex environments. While on-road autonomy has seen a major expansion in recent years in the consumer space, off-road systems are mostly relegated to niche applications. However, these applications can provide safety and navigation to dangerous areas that are the most suited for autonomy tasks. Traversability analysis is at the core of many of the algorithms employed in these topics. In this thesis, a Clearpath Robotics Jackal vehicle is equipped with a 3D Ouster laser scanner to define and traverse off-road environments. The Mississippi State University Autonomous Vehicle Simulator (MAVS) and the Navigating All Terrains Using Robotic Exploration (NATURE) autonomy stack are used in conjunction with the small-scale vehicle platform to traverse uneven terrain and collect data. Additionally, the NATURE stack is used as a point of comparison between a MAVS simulated and physical Clearpath Robotics Jackal vehicle in testing