Autonomous navigation in GNSS-denied environments remains a core challenge for legged robots, where exteroceptive sensors such as LiDAR are prone to elevation drift in geometrically sparse or repetitive scenes. We present a factor graph architecture that augments the LIO-SAM framework with a parallel kinematic lane driven by proprioceptive leg odometry, coupled to the main LiDAR-inertial lane via an identity relative pose constraint with a selective noise model. Applied to a Linxai D50 quadruped platform across two outdoor loops totaling over one kilometer, our approach reduces elevation drift from over 30m to under 30cm and enables convergence in a scene where the baseline pipeline fails entirely. These results suggest that proprioceptive data, already computed onboard for gait control, constitutes a lightweight and effective vertical anchor for SLAM in GNSS-denied settings.
This paper presents a methodology for an autonomous agent to map an unknown scalar field in GPS-denied regions. To reduce localization errors, the agent alternates between GPS-enabled and GPS-denied areas while collecting measurements. User-defined error bounds determine the dwell time in each region. A switching trajectory is then designed to ensure field measurements in GPS-denied regions remain within the specified error limits. A Lyapunov-based stability analysis guarantees bounded error trajectories while tracking the desired path. The effectiveness of the proposed methodology is demonstrated through simulations, with an error analysis comparing the GP-predicted scalar field model to the actual field.
Accurate timing and synchronization, typically enabled by GPS, are essential for modern wireless communication systems. However, many emerging applications must operate in GPS-denied environments where signals are unreliable or disrupted, resulting in oscillator drift and carrier frequency impairments. To address these challenges, we present BenchLink, a System-on-Chip (SoC)-based benchmark for resilient communication links that functions without GPS and supports adaptive pilot density and modulation. Unlike traditional General Purpose Processor (GPP)-based software-defined radios (e.g. USRPs), the SoC-based design allows for more precise latency control. We implement and evaluate BenchLink on Zynq UltraScale+ MPSoCs, and demonstrate its effectiveness in both ground and aerial environments. A comprehensive dataset has also been collected under various conditions. We will make both the SoC-based link design and dataset available to the wireless community. BenchLink is expected to facilitate future research on data-driven link adaptation, resilient synchronization in GPS-denied scenarios, and emerging applications that require precise latency control, such as integrated radar sensing
Deep Reinforcement learning (DRL) is used to enable autonomous navigation in unknown environments. Most research assume perfect sensor data, but real-world environments may contain natural and artificial sensor noise and denial. Here, we present a benchmark of both well-used and emerging DRL algorithms in a navigation task with configurable sensor denial effects. In particular, we are interested in comparing how different DRL methods (e.g. model-free PPO vs. model-based DreamerV3) are affected by sensor denial. We show that DreamerV3 outperforms other methods in the visual end-to-end navigation task with a dynamic goal - and other methods are not able to learn this. Furthermore, DreamerV3 generally outperforms other methods in sensor-denied environments. In order to improve robustness, we use adversarial training and demonstrate an improved performance in denied environments, although this generally comes with a performance cost on the vanilla environments. We anticipate this benchmark of different DRL methods and the usage of adversarial training to be a starting point for the development of more elaborate navigation strategies that are capable of dealing with uncertain and denied
Smart Micro Aerial Vehicles (MAVs) have transformed infrastructure inspection by enabling efficient, high-resolution monitoring at various stages of construction, including hard-to-reach areas. Traditional manual operation of drones in GPS-denied environments, such as industrial facilities and infrastructure, is labour-intensive, tedious and prone to error. This study presents an innovative framework for smart MAV inspections in such complex and GPS-denied indoor environments. The framework features a hierarchical perception and planning system that identifies regions of interest and optimises task paths. It also presents an advanced MAV system with enhanced localisation and motion planning capabilities, integrated with Neural Reconstruction technology for comprehensive 3D reconstruction of building structures. The effectiveness of the framework was empirically validated in a 4,000 square meters indoor infrastructure facility with an interior length of 80 metres, a width of 50 metres and a height of 7 metres. The main structure consists of columns and walls. Experimental results show that our MAV system performs exceptionally well in autonomous inspection tasks, achieving a 100\% s
Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an arc-length-based map matching method that uses a digital 2D map of the scenario in order to correct drift in the dead reckoning estimate. The kinematic model's prediction is used to introduce a temporal notion to the spatial information available in the map data. Results show reliable improvement in drift for all GPS-denied scenarios tested in this study. This innovative approach ensures that automated vehicles can maintain continuous and reliable navigation, significantly enhancing their safety and operational reliability in environments where GPS signals are compromised or unavailable.
A countable discrete group is called Choquet-Deny if for any non-degenerate probability measure on the group, the corresponding space of bounded harmonic functions is trivial. Building on the previous work of Jaworski, a complete characterization of Choquet-Deny groups was recently achieved by Frisch, Hartman, Tamuz, and Ferdowski. In this article, we extend the study of the Choquet-Deny property to the framework of discrete measured groupoids. Our primary result offers a complete characterization of this property in terms of the isotropy groups and the equivalence relation associated with the given groupoid. Additionally, we use the implications derived from our main theorem to classify the Choquet-Deny property of transformation groupoids.
For an arbitrary regular Dirichlet form $\mathscr{E}$ and the associated symmetric Markovian semigroup $T_t$, we consider the corresponding Sobolev-Bregman form $\mathscr{E}_p(u) = -\tfrac{1}{p} \frac{d}{d t}\bigr\vert_{t = 0} \|T_t u\|_p^p$, where $p \in (1, \infty)$. We prove a variant of the Beurling-Deny formula for $\mathscr{E}_p$. As an application, we prove the corresponding Hardy-Stein identity. Our results extend previous works in this area, which either required that $\mathscr{E}$ is translation-invariant, or that $u$ is sufficiently regular.
This work addresses the challenge of developing a localization system for an uncrewed ground vehicle (UGV) operating autonomously in unstructured outdoor Global Navigation Satellite System (GNSS)-denied environments. The goal is to enable accurate mapping and long-range navigation with practical applications in domains such as autonomous construction, military engineering missions, and exploration of non-Earth planets. The proposed system - Terrain-Referenced Assured Engineer Localization System (TRAELS) - integrates pose estimates produced by two complementary terrain referenced navigation (TRN) methods with wheel odometry and inertial measurement unit (IMU) measurements using an Extended Kalman Filter (EKF). Unlike simultaneous localization and mapping (SLAM) systems that require loop closures, the described approach maintains accuracy over long distances and one-way missions without the need to revisit previous positions. Evaluation of TRAELS is performed across a range of environments. In regions where a combination of distinctive geometric and ground surface features are present, the developed TRN methods are leveraged by TRAELS to consistently achieve an absolute trajectory e
Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSm9eM.
Siméon-Denis Poisson was 25 years old when he was appointed Professor of Mathematics at the École Polytechnique in 1806. Elected to the Paris Académie des Sciences six years later, he soon became one of its most influential members. The origin and later developments of the many concepts in mathematics and physics that bear his name make interesting stories, a few of which we shall attempt to sketch in this paper.
First introduced by J. Deny, the classical principle of positivity of mass states that if $κ_αμ\leqslantκ_αν$ everywhere on $\mathbb{R}^n$, then $μ(\mathbb{R}^n)\leqslantν(\mathbb{R}^n)$. Here $μ,ν$ are positive Radon measures on $\mathbb{R}^n$, $n\geqslant2$, and $κ_αμ$ is the potential of $μ$ with respect to the Riesz kernel $|x-y|^{α-n}$ of order $α\in(0,2]$, $α<n$. We strengthen Deny's principle by showing that $μ(\mathbb{R}^n)\leqslantν(\mathbb{R}^n)$ still holds even if $κ_αμ\leqslantκ_αν$ is fulfilled only on a proper subset $A$ of $\mathbb{R}^n$ that is not inner $α$-thin at infinity; and moreover, this condition on $A$ cannot in general be improved. Hence, if $ξ$ is a signed measure on $\mathbb{R}^n$ with $\int1\,dξ>0$, then $κ_αξ>0$ everywhere on $\mathbb{R}^n$, except for a subset which is inner $α$-thin at infinity. The analysis performed is based on the author's recent theories of inner Riesz balayage and inner Riesz equilibrium measures (Potential Anal., 2022), the inner equilibrium measure being understood in an extended sense where both the energy and the total mass may be infinite.
A new theory suggests the universe is constantly recording its own history in the fabric of spacetime。 If correct, this cosmic memory could help solve some of the biggest puzzles in physics, from black holes to dark matter and the universe’s ultimate fate
A distant galaxy nicknamed Shadow Blaster may have revealed a surprising source of cosmic neutrinos: extreme star formation instead of a supermassive black hole。 The discovery suggests that hidden, dust-filled starburst galaxies could account for a significant fraction of the Universe’s high-energy neutrinos
Using the Keck Observatory, astronomers measured the spins of dozens of giant planets and brown dwarfs orbiting distant stars。 They found that giant planets can spin faster than much more massive brown dwarfs, challenging simple assumptions about mass and rotation。 The results suggest that magnetic fields and formation processes play a major role i
A groundbreaking superconducting X-ray spectrometer has begun operation at BESSY II, giving Europe its first TES-based system and boosting photon detection efficiency by up to 1,000 times。 The advance enables scientists to explore atomically thin materials, nanostructures, and ultra-dilute samples with remarkable speed and sensitivity
A rare meteorite has revealed evidence of a massive lost world that once orbited the young Sun before being destroyed in a catastrophic collision。 The discovery suggests some early planets formed from dramatically different materials than Earth and Mars, rewriting part of the solar system’s origin story
Researchers gave top AI models a classic attention test used in psychology and found a major flaw。 While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex。 Some leading systems fell from over 90% accuracy to nearly complete failure
Physicists have solved a long-standing problem involving systems that appear to violate Newton’s third law, such as bird flocks and bacterial swarms。 By adding carefully designed “imaginary partners” to their models, they can now simulate these complex systems with unprecedented accuracy