Tootfinder

Robust Radar SLAM for Vehicle Parking Applications
Luis Diener, Jens Kalkkuhl, Markus Enzweiler
https://arxiv.org/abs/2509.07683 https://arxiv.org/pdf/2509…

Robust Radar SLAM for Vehicle Parking Applications
We address ego-motion estimation for automated parking, where centimeter-level accuracy is crucial due to tight spaces and nearby obstacles. Traditional methods using inertial-measurement units and wheel encoders require calibration, making them costly and time-consuming. To overcome this, we propose a radar-based simultaneous localization and mapping (SLAM) approach that leverages the robustness of radar to adverse weather and support for online calibration. Our robocentric formulation fuses f…

That's a first for me. I own 3 cargo bikes; never broke a spoke before. Somehow my wife broke 5 on one of the #CargoBikes. She was apparently carrying a(n "overweight") coworker in it, so maybe that did it?
Though when I got the bike, it had a broken rear (enviolo) hub and multiple broke spokes. I probably reused the non-broken spokes (and I definitely reused the rim),…

Bike Friends, uhhh… my wheel fell off? The fork slid apart. How much of a concern is this? I rode with no issues but I do hang my bike by the front tire all the time.
#biking #bikes #bikeTooter…

A while ago, I've followed the example given by #Fedora and unbundled ensurepip wheels from #Python in #Gentoo (just checked — "a while ago" was 3 years ago). This had the important advantage that it enabled us to update these wheels along with the actual pip and setuptools packages, meaning new virtual environments would get fresh versions rather than whatever CPython happened to bundle at the time of release.
I had considered using our system packages to prepare these wheels, but since we were already unbundling dependencies back then, that couldn't work. So I just went with fetching upstream wheels from PyPI. Why not build them from source instead? Well, besides feeling unnecessary (it's not like the PyPI wheels are actually binary packages), we probably didn't have the right kind of eclass support for that at the time.
Inspired by @…, today I've tried preparing new revisions of ensurepip packages that actually do build everything from source. So what changed, and why should building from source matter now? Firstly, as part of the wheel reuse patches, we do have a reasonably clean architecture to grab the wheels created as part of the PEP517 build. Secondly, since we're unbundling dependencies from pip and setuptools, we're effectively testing different packages than these installed as ensurepip wheels — and so it would be meaningful to test both variants. Thirdly, building from source is going to make patching easier, and at the very least enable user patching.
While at it, I've refreshed the test suite runs in all three regular packages (pip, setuptools and wheel — we need an "ensurepip" wheel for the last because of test suites). And of course, I hit some test failures in testing the versions with bundled dependencies, and I've discovered a random bug in #PyPy.
https://github.com/gentoo/gentoo/pull/42882 (yes, we haven't moved yet)
https://github.com/pypy/pypy/issues/5306

Me (to myself): Get in to work early this morning, with no messing about on the way in. Just 'a to b', fast and efficient!
Also me: [Leaves very late, grabs a unicycle and proceeds to cycling in via the following route]
🤷
https://www.strava.com/activities/14824892

Soon, I’ll be throwing away all my desktop computers as well, though I’m keeping my Lenovo Thinkpad around. I hate computers, yet I use them.
To me, computers are just junk nobody asked for. They’re glowing traps that keep us hooked, scrolling and clicking away our time for endless feeds. No matter the OS or hardware, it’s all distraction from reality.
Yes, Linux can free us and all that, and even the hardware can be part of that freedom. But the computers themselves remain a cir…

Real-time Testing of Satellite Attitude Control With a Reaction Wheel Hardware-In-the-Loop Platform
Morokot Sakal, George Nehma, Camilo Riano-Rios, Madhur Tiwari
https://arxiv.org/abs/2508.19164

Real-time Testing of Satellite Attitude Control With a Reaction Wheel Hardware-In-the-Loop Platform
We propose the Hardware-in-the-Loop (HIL) test of an adaptive satellite attitude control system with reaction wheel health estimation capabilities. Previous simulations and Software-in-the-Loop testing have prompted further experiments to explore the validity of the controller with real momentum exchange devices in the loop. This work is a step toward a comprehensive testing framework for validation of spacecraft attitude control algorithms. The proposed HIL testbed includes brushless DC motors…

Truly miffed at Amazon MGM for axing Wheel of Time after the utterly excellent season 3. A shame that many dropped off during the first season, and it was admittedly quite uneven at times, but season 2 was better and season 3 was fantastic.

The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics
Nicolas Soncini, Javier Cremona, Erica Vidal, Maximiliano Garc\'ia, Gast\'on Castro, Taih\'u Pire
https://arxiv.org/abs/2508.21635

The Rosario Dataset v2: Multimodal Dataset for Agricultural Robotics
We present a multi-modal dataset collected in a soybean crop field, comprising over two hours of recorded data from sensors such as stereo infrared camera, color camera, accelerometer, gyroscope, magnetometer, GNSS (Single Point Positioning, Real-Time Kinematic and Post-Processed Kinematic), and wheel odometry. This dataset captures key challenges inherent to robotics in agricultural environments, including variations in natural lighting, motion blur, rough terrain, and long, perceptually alias…

New on #Quansight PBC blog: Python Wheels: from Tags to Variants
#Python distributions are uniform across different Python versions and platforms. For these distributions, it is sufficient to publish a single wheel that can be installed everywhere. However, some packages are more complex than that; they include compiled Python extensions or binaries. In order to robustly deploy these software on different platforms, you need to publish multiple binary packages, and the installers need to select the one that fits the platform used best.
For a long time, Python wheels made do with a relatively simple mechanism to describe the needed variance: Platform compatibility tags. These tags identified different Python implementations and versions, operating systems, and CPU architectures. Over time, they were extended to facilitate new use cases. To list a couple: PEP 513 added manylinux tags to standardize the core library dependencies on GNU/Linux systems, and PEP 656 added musllinux tags to facilitate Linux systems with musl libc.
However, not all new use cases can be handled effectively within the framework of tags. To list a few:
• The advent of GPU-backed computing made distinguishing different acceleration frameworks such as NVIDIA CUDA or AMD ROCm important.
• As the compatibility with older CPUs became less desirable, many distributions have set baselines for their binary packages to x86-64-v2 microarchitecture level, and Python packages need to be able to express the same requirement.
• Numerical libraries support different BLAS/LAPACK, MPI, OpenMP providers, and wish to enable the users to choose the build matching their desired provider.
While tags could technically be bent to facilitate all these use cases, they would grow quite baroque, and, critically, every change to tags needs to be implemented in all installers and package-related tooling separately, making the adoption difficult.
Facing these limitations, software vendors have employed different solutions to work around the lack of an appropriate mechanism. Eventually, the #WheelNext initiative took up the challenge to design a more robust solution.
"""
#packaging

Probabilistic Collision Risk Estimation through Gauss-Legendre Cubature and Non-Homogeneous Poisson Processes
Trent Weiss, Madhur Behl
https://arxiv.org/abs/2507.18819 https://

Probabilistic Collision Risk Estimation through Gauss-Legendre Cubature and Non-Homogeneous Poisson Processes
Overtaking in high-speed autonomous racing demands precise, real-time estimation of collision risk; particularly in wheel-to-wheel scenarios where safety margins are minimal. Existing methods for collision risk estimation either rely on simplified geometric approximations, like bounding circles, or perform Monte Carlo sampling which leads to overly conservative motion planning behavior at racing speeds. We introduce the Gauss-Legendre Rectangle (GLR) algorithm, a principled two-stage integratio…

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Miguel \'A. Gonz\'alez-Santamarta, Francisco J. Rodr\'iguez-Lera, Vicente Matell\'an-Olivera
https://arxiv.org/abs/2506.09583

VAULT: A Mobile Mapping System for ROS 2-based Autonomous Robots
Localization plays a crucial role in the navigation capabilities of autonomous robots, and while indoor environments can rely on wheel odometry and 2D LiDAR-based mapping, outdoor settings such as agriculture and forestry, present unique challenges that necessitate real-time localization and consistent mapping. Addressing this need, this paper introduces the VAULT prototype, a ROS 2-based mobile mapping system (MMS) that combines various sensors to enable robust outdoor and indoor localization.…