TRAAC: Trust and Risk Aware Access Control

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IMU as an Input vs. a Measurement of the State in Inertial-Aided State Estimation

In this technical report, we compare treating an IMU as an input to a motion model against treating it as a measurement of the state in a continuous-time state estimation framework. Treating IMU measurements as inputs to a motion model and then preintegrating these measurements has almost become a de-facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case of IMU measurement dropout. Third, it does not lend itself well to dealing with multiple high-rate sensors such as a lidar and an IMU or multiple IMUs. In this work, we methodically compare the performance of these two approaches on a 1D simulation and show that they perform identically, assuming that each method's hyperparameters have been tuned on a training set. We show how to preintegrate heterogeneous factors using Gaussian process interpolation. We also provide results for our continuous-time lidar-inertial odometry in simulation and on the Newer College Dataset. Code for our lidar-inertial odometry can be found at: https://github.com/utiasASRL/steam_icpComment: Submitted to Robotica March 9th, 202

Translating Race : Mission Hymns and the Challenge of Christian Identity

“Ye seed of Israel’s chosen race,” “The race that long in darkness pined,” “To heal and save a race undone,” and “Sanctify a ransomed race” are a few examples of many references to “race” that exist in English-language hymnody. Throughout the nineteenth-century, hymns containing lines such as these, were exported from Britain into mission fields where translators had to find new ways to conceptualize notions of race and, in effect, created new group identities. This requires asking critical questions about the implications of what happened when ideas of race, in the Christian sense, interacted with non-religious notions of race in the colonial contexts where these missions were established, how ideas of race had to be rethought and were received in the colonial settings where missions were often established. Accordingly, this article explores the different notions of race expressed in English-language hymnody and, with reference to specific examples from various settings around the world, to show how missionary translators dealt with notions of race, how the term was expressed in Indigenous languages, how ideas of race were received and understood, and the implications this had for the creation of new Christian communities

Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights

This paper presents a novel deep-learning-based approach to improve localizing radar measurements against lidar maps. Although the state of the art for localization is matching lidar data to lidar maps, radar has been considered as a promising alternative, as it is potentially more resilient against adverse weather such as precipitation and heavy fog. To make use of existing high-quality lidar maps, while maintaining performance in adverse weather, matching radar data to lidar maps is of interest. However, owing in part to the unique artefacts present in radar measurements, radar-lidar localization has struggled to achieve comparable performance to lidar-lidar systems, preventing it from being viable for autonomous driving. This work builds on an ICP-based radar-lidar localization system by including a learned preprocessing step that weights radar points based on high-level scan information. Combining a proven analytical approach with a learned weight reduces localization errors in radar-lidar ICP results run on real-world autonomous driving data by up to 54.94% in translation and 68.39% in rotation, while maintaining interpretability and robustness.Comment: 8 pages (6 content, 2 references). 4 figures, submitted to the 2024 IEEE International Conference on Robotics and Automation (ICRA