Publications | Christopher Grebe

Publications in reverse chronological order. See Google Scholar for more information.

2023

Radar SNR distribution return descriptor for use in object detection and ground clutter removal

Christopher Grebe

2023

U.S. Patent Application 20240402325, filed May 30, 2023

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Disclosed are techniques for distinguishing potential obstacles from ground clutter using the signal-to-noise-ratio (SNR). A first set of points or group of points with a high SNR, at least partially surrounded by points with a low SNR, is classified as a potential obstacle. A second set of points or groups of points with a low SNR is classified as ground clutter.
Monostatic scanning lidar using a multi-faceted polygon mirror as one of dual redirecting elements

Derek Butler , Philip Church , Christopher Grebe, and Michael Sekerka-bajbus

2023

Canadian Patent No. CA 3136636, issued May 2, 2023

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A sensor comprises two independently rotatable elements. The first element comprises facets in a polygonal configuration fully rotatable about a first axis at a first angle relative to a source’s beam axis and redirects energy incident on a facet at a second angle to a facet plane at a reflected angle equal in magnitude to the second angle as the first element is rotated. The second element may be a wedge mirror fully and independently rotatable about a second axis at a third angle to the beam axis that redirects energy at a fourth angle to the second axis, in a direction within the FOV, receives reflected energy to the first element for redirection toward an element interposed between it and the source that allows the source energy to pass unimpeded, and on to a detector. Correlating data from the detector and the source determines the target range.

2022

Learning to detect slip with barometric tactile sensors and a temporal convolutional neural network

Abhinav Grover , Philippe Nadeau , Christopher Grebe, and Jonathan Kelly

In 2022 International Conference on Robotics and Automation (ICRA) , 2022

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The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, off-the-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.
@inproceedings{grover2022learning, title = {Learning to detect slip with barometric tactile sensors and a temporal convolutional neural network}, author = {Grover, Abhinav and Nadeau, Philippe and Grebe, Christopher and Kelly, Jonathan}, booktitle = {2022 International Conference on Robotics and Automation (ICRA)}, pages = {570--576}, year = {2022}, organization = {IEEE}, doi = {10.1109/ICRA46639.2022.9811592}, google_scholar_id = {UeHWp8X0CEIC}, }
A study of observability-aware trajectory optimization

Christopher Grebe

University of Toronto (Canada) , 2022

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Ideally, robots should move in ways that maximize knowledge gained about the state of both their internal system and external operating environment. Recently, observability-based metrics have been proposed to find trajectories that enable rapid and accurate estimation. A system is observable, roughly, if relevant states and parameters can be recovered from measurements over finite time. Degree of observability has been applied as a metric to optimize motion to produce more observable trajectories that yield better estimation accuracy. The viability of methods for observability-aware trajectory optimization are not yet well understood in the literature. In this thesis, we compare two state-of-the-art methods for trajectory optimization and seek to add important theoretical clarifications and valuable discussion about their effectiveness. For evaluation, we examine the representative task of sensor-to-sensor extrinsic self-calibration using a realistic physics simulator. We also study the sensitivity of these algorithms to changes in information content of exteroceptive sensor measurements.
@phdthesis{grebe2022study, title = {A study of observability-aware trajectory optimization}, author = {Grebe, Christopher}, year = {2022}, school = {University of Toronto (Canada)}, google_scholar_id = {zYLM7Y9cAGgC}, }

2021

Observability-aware trajectory optimization: Theory, viability, and state of the art

Christopher Grebe, Emmett Wise , and Jonathan Kelly

In 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) , 2021

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Ideally, robots should move in ways that maximize the knowledge gained about the state of both their internal system and the external operating environment. Trajectory design is a challenging problem that has been investigated from a variety of perspectives, ranging from information-theoretic analyses to learning-based approaches. Recently, observability- based metrics have been proposed to find trajectories that enable rapid and accurate state and parameter estimation. The viability and efficacy of these methods is not yet well understood in the literature. In this paper, we compare two state-of-the-art methods for observability-aware trajectory optimization and seek to add important theoretical clarifications and valuable discussion about their overall effectiveness. For evaluation, we examine the representative task of sensor-to-sensor extrinsic self-calibration using a realistic physics simulator. We also study the sensitivity of these algorithms to changes in the information content of the exteroceptive sensor measurements.
@inproceedings{grebe2021observability, title = {Observability-aware trajectory optimization: Theory, viability, and state of the art}, author = {Grebe, Christopher and Wise, Emmett and Kelly, Jonathan}, booktitle = {2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)}, pages = {1--8}, year = {2021}, organization = {IEEE}, google_scholar_id = {qjMakFHDy7sC}, }
A question of time: Revisiting the use of recursive filtering for temporal calibration of multisensor systems

Jonathan Kelly , Christopher Grebe, and Matthew Giamou

In 2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) , 2021

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We examine the problem of time delay estimation, or temporal calibration, in the context of multisensor data fusion. Differences in processing intervals and other factors typically lead to a relative delay between measurement updates from disparate sensors. Correct (optimal) data fusion demands that the relative delay must either be known in advance or identified online. There have been several recent proposals in the literature to determine the delay using recursive, causal filters such as the extended Kalman filter (EKF). We carefully review this formulation and show that there are fundamental issues with the structure of the EKF (and related algorithms) when the delay is included in the filter state vector as a parameter to be estimated. These structural issues, in turn, leave recursive filters prone to bias and inconsistency. Our theoretical analysis is supported by simulation studies that demonstrate the implications in terms of filter performance; although tuning of the filter noise variances may reduce the chance of inconsistency or divergence, the underlying structural concerns remain. We offer brief suggestions for ways to maintain the computational efficiency of recursive filtering for temporal calibration while avoiding the drawbacks of the standard filtering algorithms.
@inproceedings{kelly2021question, title = {A question of time: Revisiting the use of recursive filtering for temporal calibration of multisensor systems}, author = {Kelly, Jonathan and Grebe, Christopher and Giamou, Matthew}, booktitle = {2021 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)}, pages = {1--8}, year = {2021}, organization = {IEEE}, google_scholar_id = {2osOgNQ5qMEC}, }
Under pressure: Learning to detect slip with barometric tactile sensors

Abhinav Grover , Christopher Grebe, Philippe Nadeau , and Jonathan Kelly

arXiv preprint arXiv:2103.13460, 2021

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Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings. Part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direction of the slip motion. Further, we test our detector on two robot manipulation tasks involving common household objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for complex manipulation tasks such as slip compensation.
@article{grover2021under, title = {Under pressure: Learning to detect slip with barometric tactile sensors}, author = {Grover, Abhinav and Grebe, Christopher and Nadeau, Philippe and Kelly, Jonathan}, journal = {arXiv preprint arXiv:2103.13460}, year = {2021}, google_scholar_id = {9yKSN-GCB0IC}, }
A continuous-time approach for 3d radar-to-camera extrinsic calibration

Emmett Wise , Juraj Peršić , Christopher Grebe, Ivan Petrović , and 1 more author

In 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021

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Reliable operation in inclement weather is essential to the deployment of safe autonomous vehicles (AVs). Robustness and reliability can be achieved by fusing data from the standard AV sensor suite (i.e., lidars, cameras) with weather robust sensors, such as millimetre-wavelength radar. Critically, accurate sensor data fusion requires knowledge of the rigidbody transform between sensor pairs, which can be determined through the process of extrinsic calibration. A number of extrinsic calibration algorithms have been designed for 2D (planar) radar sensors—however, recently-developed, low-cost 3D millimetre-wavelength radars are set to displace their 2D counterparts in many applications. In this paper, we present a continuous-time 3D radar-to-camera extrinsic calibration algorithm that utilizes radar velocity measurements and, unlike the majority of existing techniques, does not require specialized radar retroreflectors to be present in the environment. We derive the observability properties of our formulation and demonstrate the efficacy of our algorithm through synthetic and real-world experiments.
@inproceedings{wise2021continuous, title = {A continuous-time approach for 3d radar-to-camera extrinsic calibration}, author = {Wise, Emmett and Per{\v{s}}i{\'c}, Juraj and Grebe, Christopher and Petrovi{\'c}, Ivan and Kelly, Jonathan}, booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)}, pages = {13164--13170}, year = {2021}, organization = {IEEE}, google_scholar_id = {d1gkVwhDpl0C}, }

2018

Aerial and surface security applications using lidar

Philip Church , Christopher Grebe, Justin Matheson , and Brett Owens

Laser Radar Technology and Applications XXIII, 2018

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This paper reviews early evaluations of the Neptec Technologies 3D LiDAR sensor’s capabilities for the security markets related to aerial and surface threats. Aerial threats are primarily focused on drone detection, while surface threats include perimeter security on the ground or over water. The OPAL LiDAR uses a Risley-prism pair mechanism to generate unique scan patterns, offering the advantage of rapid and tight coverage of the Field-of-View (FOV). Field trials were conducted for characterizing the detection capability of small drones, such as the DJI Phantom-3. The main variables for the testing included; distance from sensor to the drone, speed, and trajectory as well as specific LiDAR intrinsic settings. Similar field trials have been conducted for perimeter incursions over land and water. A predictive model has been developed for the probability of detection of small targets, taking into account the LiDAR’s optomechanical settings in relation to the target size and reflectivity. The results obtained from these trials is presented and compared to the predictive model.
@article{church2018aerial, title = {Aerial and surface security applications using lidar}, author = {Church, Philip and Grebe, Christopher and Matheson, Justin and Owens, Brett}, journal = {Laser Radar Technology and Applications XXIII}, volume = {10636}, pages = {27--38}, year = {2018}, publisher = {SPIE}, google_scholar_id = {u5HHmVD_uO8C}, }
A new real-time signal processing approach for frequency-varying machinery

Jie Zhang , Hongli Gao , Qiyue Liu , and Christopher Grebe

Journal of Vibration and Control, 2018

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Development of condition monitoring approaches has played a key role in the stability and safety of frequency-varying machinery operations. Conventional time–frequency analysis methods suffer problems such as analysis results being too complex to realize highly intelligent and automated condition monitoring systems. Blind source separation is an attractive tool due to its excellent performance in separating defect source signals from their mixtures without detailed knowledge of sources and mixing processes; however, it can only be applied under some strict conditions. In this paper, a nonuniform sampling model is built and a new processing algorithm of frequency-varying signal is proposed. The relationship between the power spectral density (PSD) of the vibration signal of frequency-varying machinery and frequencies at different rotational speeds is derived. The proposed method can adaptively eliminate the influence of the varying rotational speed in the revised PSD. Some classical signal analysis methods are implemented to compare with the proposed approach by simulations. An experiment has been conducted by using a JD-1 wheel/rail simulation facility to illustrate the effectiveness of the proposed method.
@article{zhang2018new, title = {A new real-time signal processing approach for frequency-varying machinery}, author = {Zhang, Jie and Gao, Hongli and Liu, Qiyue and Grebe, Christopher}, journal = {Journal of Vibration and Control}, volume = {24}, number = {11}, pages = {2359--2368}, year = {2018}, publisher = {SAGE Publications Sage UK: London, England}, google_scholar_id = {u-x6o8ySG0sC}, }