11 research outputs found
GMCR: Graph-based Maximum Consensus Estimation for Point Cloud Registration
Point cloud registration is a fundamental and challenging problem for
autonomous robots interacting in unstructured environments for applications
such as object pose estimation, simultaneous localization and mapping,
robot-sensor calibration, and so on. In global correspondence-based point cloud
registration, data association is a highly brittle task and commonly produces
high amounts of outliers. Failure to reject outliers can lead to errors
propagating to downstream perception tasks. Maximum Consensus (MC) is a widely
used technique for robust estimation, which is however known to be NP-hard.
Exact methods struggle to scale to realistic problem instances, whereas high
outlier rates are challenging for approximate methods. To this end, we propose
Graph-based Maximum Consensus Registration (GMCR), which is highly robust to
outliers and scales to realistic problem instances. We propose novel consensus
functions to map the decoupled MC-objective to the graph domain, wherein we
find a tight approximation to the maximum consensus set as the maximum clique.
The final pose estimate is given in closed-form. We extensively evaluated our
proposed GMCR on a synthetic registration benchmark, robotic object
localization task, and additionally on a scan matching benchmark. Our proposed
method shows high accuracy and time efficiency compared to other
state-of-the-art MC methods and compares favorably to other robust registration
methods.Comment: Accepted at icra 202
An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation
Accurate object pose estimation using multi-modal perception such as visual and tactile sensing have been used for autonomous robotic manipulators in literature. Due to variation in density of visual and tactile data, we previously proposed a novel probabilistic Bayesian filter-based approach termed translation-invariant Quaternion filter (TIQF) for pose estimation. As tactile data collection is time consuming, active tactile data collection is preferred by reasoning over multiple potential actions for maximal expected information gain. In this paper, we empirically evaluate various information gain criteria for action selection in the context of object pose estimation. We demonstrate the adaptability and effectiveness of our proposed TIQF pose estimation approach with various information gain criteria. We find similar performance in terms of pose accuracy with sparse measurements across all the selected criteria
Intelligent in-vehicle interaction technologies
With rapid advances in the field of autonomous vehicles (AVs), the ways in which human–vehicle interaction (HVI) will take place inside the vehicle have attracted major interest and, as a result, intelligent interiors are being explored to improve the user experience, acceptance, and trust. This is also fueled by parallel research in areas such as perception and control of robots, safe human–robot interaction, wearable systems, and the underpinning flexible/printed electronics technologies. Some of these are being routed to AVs. Growing number of network of sensors are being integrated into the vehicles for multimodal interaction to draw correct inferences of the communicative cues from the user and to vary the interaction dynamics depending on the cognitive state of the user and contextual driving scenario. In response to this growing trend, this timely article presents a comprehensive review of the technologies that are being used or developed to perceive user's intentions for natural and intuitive in-vehicle interaction. The challenges that are needed to be overcome to attain truly interactive AVs and their potential solutions are discussed along with various new avenues for future research
An Empirical Evaluation of Various Information Gain Criteria for Active Tactile Action Selection for Pose Estimation
Accurate object pose estimation using multi-modal perception such as visual
and tactile sensing have been used for autonomous robotic manipulators in
literature. Due to variation in density of visual and tactile data, we
previously proposed a novel probabilistic Bayesian filter-based approach termed
translation-invariant Quaternion filter (TIQF) for pose estimation. As tactile
data collection is time consuming, active tactile data collection is preferred
by reasoning over multiple potential actions for maximal expected information
gain. In this paper, we empirically evaluate various information gain criteria
for action selection in the context of object pose estimation. We demonstrate
the adaptability and effectiveness of our proposed TIQF pose estimation
approach with various information gain criteria. We find similar performance in
terms of pose accuracy with sparse measurements across all the selected
criteria.Comment: arXiv admin note: substantial text overlap with arXiv:2109.1354
Active Visuo-Tactile Point Cloud Registration for Accurate Pose Estimation of Objects in an Unknown Workspace
This paper proposes a novel active visuo-tactile based methodology wherein the accurate estimation of the time-invariant SE(3) pose of objects is considered for autonomous robotic manipulators. The robot equipped with tactile sensors on the gripper is guided by a vision estimate to actively explore and localize the objects in the unknown workspace. The robot is capable of reasoning over multiple potential actions, and execute the action to maximize information gain to update the current belief of the object. We formulate the pose estimation process as a linear translation invariant quaternion filter (TIQF) by decoupling the estimation of translation and rotation and formulating the update and measurement model in linear form. We perform pose estimation sequentially on acquired measurements using very sparse point cloud (≤ 15 points) as acquiring each measurement using tactile sensing is time consuming. Furthermore, our proposed method is computationally efficient to perform an exhaustive uncertainty-based active touch selection strategy in real-time without the need for trading information gain with execution time. We evaluated the performance of our approach extensively in simulation and by a robotic system
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation
Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents such as autonomous vehicles and robots alike to operate effectively in unstructured environments. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows to minimise the discrepancy between dense and sparse point clouds with minimal unlabelled sparse point clouds, thereby saving additional sparse data collection, annotation and retraining costs. In this work, we propose a novel method for point cloud based object recognition with competitive performance with state-of-art methods on dense and sparse point clouds while being trained only with dense point clouds
Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation
Three-dimensional (3D) object recognition is crucial for intelligent
autonomous agents such as autonomous vehicles and robots alike to operate
effectively in unstructured environments. Most state-of-art approaches rely on
relatively dense point clouds and performance drops significantly for sparse
point clouds. Unsupervised domain adaption allows to minimise the discrepancy
between dense and sparse point clouds with minimal unlabelled sparse point
clouds, thereby saving additional sparse data collection, annotation and
retraining costs. In this work, we propose a novel method for point cloud based
object recognition with competitive performance with state-of-art methods on
dense and sparse point clouds while being trained only with dense point clouds
Deep active cross-modal visuo-tactile transfer learning for robotic object recognition
We proposeforthe firsttime, a novel deep active visuotactile cross-modal full-fledged framework for object recognition by autonomous robotic systems. Our proposed network xAVTNet is actively trained with labelled point clouds from a vision sensor with one robot and tested with an active tactile perception strategy to recognise objects never touched before using another robot. We propose a novel visuo-tactile loss (VTLoss) to minimise the discrepancy between the visual and tactile domains for unsupervised domain adaptation.Our framework leverages the strengths of deep neural networks for cross-modal recognition along with active perception and active learning strategies for increased efficiency by minimising redundant data collection. Our method is extensively evaluated on a real robotic system and compared against baselines and other state-of-art approaches. We demonstrate clear outperformance in recognition accuracy compared to the state-of-art visuo-tactile cross-modal recognition method