arXiv subjects

Grace Gao

Publications and source records attributed to Grace Gao.

At least 19 recordsLinked to original sources

arXiv2026

Coverage Optimization for Camera View Selection

What makes a good viewpoint? The quality of the data used to learn 3D reconstructions is crucial for enabling efficient and accurate scene modeling. We study the active view selection problem and develop a principled analysis that yields a simple and interpretable criterion for selecting informative camera poses. Our key insight is that informative views can be obtained by minimizing a tractable approximation of the Fisher Information Gain, which reduces to favoring viewpoints that cover geometry that has been insufficiently observed by past cameras. This leads to a lightweight coverage-based view selection metric that avoids expensive transmittance estimation and is robust to noise and training dynamics. We call this metric COVER (Camera Optimization for View Exploration and Reconstruction). We integrate our method into the Nerfstudio framework and evaluate it on real datasets within fixed and embodied data acquisition scenarios. Across multiple datasets and radiance-field baselines, our method consistently improves reconstruction quality compared to state-of-the-art active view selection methods. Additional visualizations and our Nerfstudio package can be found at https://chengine.github.io/nbv_gym/.

cs.CV↗

arXiv2026

Visual SLAM with DEM Anchoring for Lunar Surface Navigation

Future lunar missions will require autonomous rovers capable of traversing tens of kilometers across challenging terrain while maintaining accurate localization and producing globally consistent maps. However, the absence of global positioning systems, extreme illumination, and low-texture regolith make long-range navigation on the Moon particularly difficult, as visual-inertial odometry pipelines accumulate drift over extended traverses. To address this challenge, we present a stereo visual simultaneous localization and mapping (SLAM) system that integrates learned feature detection and matching with global constraints from digital elevation models (DEMs). Our front-end employs learning-based feature extraction and matching to achieve robustness to illumination extremes and repetitive terrain, while the back-end incorporates DEM-derived height and surface-normal factors into a pose graph, providing absolute surface constraints that mitigate long-term drift. We validate our approach using both simulated lunar traverse data generated in Unreal Engine and real Moon/Mars analog data collected from Mt. Etna. Results demonstrate that DEM anchoring consistently reduces absolute trajectory error compared to baseline SLAM methods, lowering drift in long-range navigation even in repetitive or visually aliased terrain.

cs.RO↗

arXiv2026

Full Stack Navigation, Mapping, and Planning for the Lunar Autonomy Challenge

We present a modular, full-stack autonomy system for lunar surface navigation and mapping developed for the Lunar Autonomy Challenge. Operating in a GNSS-denied, visually challenging environment, our pipeline integrates semantic segmentation, stereo visual odometry, pose graph SLAM with loop closures, and layered planning and control. We leverage lightweight learning-based perception models for real-time segmentation and feature tracking and use a factor-graph backend to maintain globally consistent localization. High-level waypoint planning is designed to promote mapping coverage while encouraging frequent loop closures, and local motion planning uses arc sampling with geometric obstacle checks for efficient, reactive control. We evaluate our approach in the competition's high-fidelity lunar simulator, demonstrating centimeter-level localization accuracy, high-fidelity map generation, and strong repeatability across random seeds and rock distributions. Our solution achieved first place in the final competition evaluation.

cs.RO↗

arXiv2026

Neural Radiance Maps for Extraterrestrial Navigation and Path Planning

Autonomous vehicles such as the Mars rovers currently lead the vanguard of surface exploration on extraterrestrial planets and moons. In order to accelerate the pace of exploration and science objectives, it is critical to plan safe and efficient paths for these vehicles. However, current rover autonomy is limited by a lack of global maps which can be easily constructed and stored for onboard re-planning. Recently, Neural Radiance Fields (NeRFs) have been introduced as a detailed 3D scene representation which can be trained from sparse 2D images and efficiently stored. We propose to use NeRFs to construct maps for online use in autonomous navigation, and present a planning framework which leverages the NeRF map to integrate local and global information. Our approach interpolates local cost observations across global regions using kernel ridge regression over terrain features extracted from the NeRF map, allowing the rover to re-route itself around untraversable areas discovered during online operation. We validate our approach in high-fidelity simulation and demonstrate lower cost and higher percentage success rate path planning compared to various baselines.

cs.RO↗

arXiv2026

Semantic Segmentation and Depth Estimation for Real-Time Lunar Surface Mapping Using 3D Gaussian Splatting

Navigation and mapping on the lunar surface require robust perception under challenging conditions, including poorly textured environments, high-contrast lighting, and limited computational resources. This paper presents a real-time mapping framework that integrates dense perception models with a 3D Gaussian Splatting (3DGS) representation. We first benchmark several models on synthetic datasets generated with the LuPNT simulator, selecting a stereo dense depth estimation model based on Gated Recurrent Units for its balance of speed and accuracy in depth estimation, and a convolutional neural network for its superior performance in detecting semantic segments. Using ground truth poses to decouple the local scene understanding from the global state estimation, our pipeline reconstructs a 120-meter traverse with a geometric height accuracy of approximately 3 cm, outperforming a traditional point cloud baseline without LiDAR. The resulting 3DGS map enables novel view synthesis and serves as a foundation for a full SLAM system, where its capacity for joint map and pose optimization would offer significant advantages. Our results demonstrate that combining semantic segmentation and dense depth estimation with learned map representations is an effective approach for creating detailed, large-scale maps to support future lunar surface missions.

cs.CV↗

arXiv2026

GNSS-based Lunar Orbit and Clock Estimation With Stochastic Cloning UD Filter

This paper presents a terrestrial GNSS-based orbit and clock estimation framework for lunar navigation satellites. To enable high-precision estimation under the low-observability conditions encountered at lunar distances, we develop a stochastic-cloning UD-factorized filter and delayed-state smoother that provide enhanced numerical stability when processing precise time-differenced carrier phase (TDCP) measurements. A comprehensive dynamics and measurement model is formulated, explicitly accounting for relativistic coupling between orbital and clock states, lunar time-scale transformations, and signal propagation delays including ionospheric, plasmaspheric, and Shapiro effects. The proposed approach is evaluated using high-fidelity Monte-Carlo simulations incorporating realistic multi-constellation GNSS geometry, broadcast ephemeris errors, lunar satellite dynamics, and ionospheric and plasmaspheric delay computed from empirical electron density models. Simulation results demonstrate that combining ionosphere-free pseudorange and TDCP measurements achieves meter-level orbit accuracy and sub-millimeter-per-second velocity accuracy, satisfying the stringent signal-in-space error requirements of future Lunar Augmented Navigation Services (LANS).

cs.RO↗

arXiv2025

Ephemeris and Almanac Design for Lunar Navigation Satellites

This paper presents almanac and ephemeris message representation for lunar navigation satellites supporting the Lunar Augmented Navigation System (LANS). The proposed method combines osculating orbital elements, Chebyshev polynomials, and Fourier series to efficiently represent lunar satellite trajectories subject to complex perturbations from lunar gravity and third-body effects. For the ephemeris, a hybrid Chebyshev--Fourier formulation improves fitting accuracy over long arcs while maintaining message compactness under the data-size constraint of the LunaNet Interoperability Specification. For the almanac, a compact low-order polynomial and Fourier model is introduced to capture mid-term orbital variations over a 15-day fitting arc. The approach is validated for multiple orbit regimes, including 30-hour, 24-hour, and 12-hour elliptical lunar frozen orbits (ELFOs) and a 6-hour polar orbit. Results show that the proposed framework achieves sub-meter position and sub-millimeter-per-second velocity fitting errors within the 900-bit limit for 6-hour ephemeris arcs, and almanac fitting accuracy sufficient for reliable satellite-visibility identification in warm-start operations.

astro-ph.IM↗

arXiv2025

Trade-off Analysis for Lunar Augmented Navigation Service (LANS) Constellation Design

The establishment of a sustainable human presence on the Moon demands robust positioning, navigation, and timing (PNT) services capable of supporting both surface and orbital operations. This paper presents a comprehensive trade-off analysis of lunar frozen-orbit constellations for the Lunar Augmented Navigation Service (LANS), focusing on how the number of satellites and orbital parameters influence coverage, position dilution of precision (PDOP), orbit determination accuracy, receiver noise, and orbit insertion cost. Three Walker-constellation families based on frozen elliptical and circular orbits are examined to characterize their relative advantages across different semi-major axes and inclinations. Results show that larger semi-major axes enhance both polar and global coverage, though the optimal inclination depends on the constellation type and target service region. The south elliptical lunar frozen orbit (ELFO) Walker constellation provides superior performance for polar coverage and PDOP, whereas the circular lunar frozen orbit (CLFO) Walker configuration achieves the best global uniformity. Orbit determination errors and receiver noise both increase with larger semi-major axes and higher inclinations, reflecting weaker geometric observability and reduced received signal power at apolune for eccentric orbits. Orbit insertion analysis reveals clear trade-offs among transfer duration, characteristic energy ($C_3$) at trans-lunar injection, and insertion $\Delta V$: shorter transfers require higher insertion $\Delta V$, while low-energy transfers achieve smaller $\Delta V$ at the cost of months-long durations and higher $C_3$. These findings provide a systematic framework for designing LANS constellations for both regional and global coverage.

astro-ph.IM↗

arXiv2025

Ionospheric and Plasmaspheric Delay Characterization for Lunar Terrestrial GNSS Receivers with Global Core Plasma Model

Recent advancements in lunar positioning, navigation, and timing (PNT) have demonstrated that terrestrial GNSS signals, including weak sidelobe transmissions, can be exploited for lunar spacecraft positioning and timing. While GNSS-based navigation at the Moon has been validated recently, unmodeled ionospheric and plasmaspheric delays remain a significant error source, particularly given the unique signal geometry and extended propagation paths. This paper characterizes these delays using the Global Core Plasma Model (GCPM) and a custom low-cost ray-tracing algorithm that iteratively solves for bent signal paths. We simulate first-, second-, and third-order group delays, as well as excess path length from ray bending, for GNSS signals received at both lunar orbit and the lunar south pole under varying solar and geomagnetic conditions. Results show that mean group delays are typically on the order of 1 m, but can exceed 100 m for low-altitude ray paths during high solar activity, while bending delays are generally smaller but non-negligible for low-altitude ray paths. We also quantify the influence of signal frequency, geomagnetic $K_p$ index, and solar R12 index. These findings inform the design of robust positioning and timing algorithms that utilize terrestrial GNSS signals.

cs.RO↗

arXiv2025

Satellite Autonomous Clock Fault Monitoring with Inter-Satellite Ranges Using Euclidean Distance Matrices

To address the need for robust positioning, navigation, and timing services in lunar environments, this paper proposes a novel onboard clock phase jump detection framework for satellite constellations using range measurements obtained from dual one-way inter-satellite links. Our approach leverages vertex redundantly rigid graphs to detect faults without relying on prior knowledge of satellite positions or clock biases, providing flexibility for lunar satellite networks with diverse satellite types and operators. We model satellite constellations as graphs, where satellites are vertices and inter-satellite links are edges. The proposed algorithm detects and identifies satellites with clock jumps by monitoring the singular values of the geometric-centered Euclidean distance matrix (GCEDM) of 5-clique sub-graphs. The proposed method is validated through simulations of a GPS constellation and a notional constellation around the Moon, demonstrating its effectiveness in various configurations.

cs.RO↗

arXiv2024

Large-Scale GNSS Spreading Code Optimization

We propose a bit-flip descent method for optimizing binary spreading codes with large family sizes and long lengths, addressing the challenges of large-scale code design in GNSS and emerging PNT applications. The method iteratively flips code bits to improve the codes' auto- and cross-correlation properties. In our proposed method, bits are selected by sampling a small set of candidate bits and choosing the one that offers the best improvement in performance. The method leverages the fact that incremental impact of a bit flip on the auto- and cross-correlation may be efficiently computed without recalculating the entire function. We apply this method to two code design problems modeled after the GPS L1 C/A and Galileo E1 codes, demonstrating rapid convergence to low-correlation codes. The proposed approach offers a powerful tool for developing spreading codes that meet the demanding requirements of modern and future satellite navigation systems.

eess.SP↗

arXiv2024

Multi-Robot Collaborative Localization and Planning with Inter-Ranging

Robots often use feature-based image tracking to identify their position in their surrounding environment; however, feature-based image tracking is prone to errors in low-textured and poorly lit environments. Specifically, we investigate a scenario where robots are tasked with exploring the surface of the Moon and are required to have an accurate estimate of their position to be able to correctly geotag scientific measurements. To reduce localization error, we complement traditional feature-based image tracking with ultra-wideband (UWB) distance measurements between the robots. The robots use an advanced mesh-ranging protocol that allows them to continuously share distance measurements amongst each other rather than relying on the common "anchor" and "tag" UWB architecture. We develop a decentralized multi-robot coordination algorithm that actively plans paths based on measurement line-of-sight vectors amongst all robots to minimize collective localization error. We then demonstrate the emergent behavior of the proposed multi-robot coordination algorithm both in simulation and hardware to lower a geometry-based uncertainty metric and reduce localization error.

cs.RO↗

arXiv2024

Autonomous Constellation Fault Monitoring with Inter-satellite Links: A Rigidity-Based Approach

To address the need for robust positioning, navigation, and timing services in lunar environments, this paper proposes a novel fault detection framework for satellite constellations using inter-satellite ranging (ISR). Traditionally, navigation satellites can depend on a robust network of ground-based stations for fault monitoring. However, due to cost constraints, a comprehensive ground segment on the lunar surface is impractical for lunar constellations. Our approach leverages vertex redundantly rigid graphs to detect faults without relying on precise ephemeris. We model satellite constellations as graphs where satellites are vertices and inter-satellite links are edges. We identify faults through the singular values of the geometric-centered Euclidean distance matrix (GCEDM) of 2-vertex redundantly rigid sub-graphs. The proposed method is validated through simulations of constellations around the Moon, demonstrating its effectiveness in various configurations. This research contributes to the reliable operation of satellite constellations for future lunar exploration missions.

cs.RO↗

arXiv2024

Neural Elevation Models for Terrain Mapping and Path Planning

This work introduces Neural Elevations Models (NEMos), which adapt Neural Radiance Fields to a 2.5D continuous and differentiable terrain model. In contrast to traditional terrain representations such as digital elevation models, NEMos can be readily generated from imagery, a low-cost data source, and provide a lightweight representation of terrain through an implicit continuous and differentiable height field. We propose a novel method for jointly training a height field and radiance field within a NeRF framework, leveraging quantile regression. Additionally, we introduce a path planning algorithm that performs gradient-based optimization of a continuous cost function for minimizing distance, slope changes, and control effort, enabled by differentiability of the height field. We perform experiments on simulated and real-world terrain imagery, demonstrating NEMos ability to generate high-quality reconstructions and produce smoother paths compared to discrete path planning methods. Future work will explore the incorporation of features and semantics into the height field, creating a generalized terrain model.

cs.RO↗

arXiv2024

Greedy Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices

Numerous methods have been proposed for global navigation satellite system (GNSS) receivers to detect faulty GNSS signals. One such fault detection and exclusion (FDE) method is based on the mathematical concept of Euclidean distance matrices (EDMs). This paper outlines a greedy approach that uses an improved Euclidean distance matrix-based fault detection and exclusion algorithm. The novel greedy EDM FDE method implements a new fault detection test statistic and fault exclusion strategy that drastically simplifies the complexity of the algorithm over previous work. To validate the novel greedy EDM FDE algorithm, we created a simulated dataset using receiver locations from around the globe. The simulated dataset allows us to verify our results on 2,601 different satellite geometries. Additionally, we tested the greedy EDM FDE algorithm using a real-world dataset from seven different android phones. Across both the simulated and real-world datasets, the Python implementation of the greedy EDM FDE algorithm is shown to be computed an order of magnitude more rapidly than a comparable greedy residual FDE method while obtaining similar fault exclusion accuracy. We provide discussion on the comparative time complexities of greedy EDM FDE, greedy residual FDE, and solution separation. We also explain potential modifications to greedy residual FDE that can be added to alter performance characteristics.

cs.RO↗

arXiv2024

Spreading Code Optimization for Low-Earth Orbit Satellites via Mixed-Integer Convex Programming

Optimizing the correlation properties of spreading codes is critical for minimizing inter-channel interference in satellite navigation systems. By improving the codes' correlation sidelobes, we can enhance navigation performance while minimizing the required spreading code lengths. In the case of low earth orbit (LEO) satellite navigation, shorter code lengths (on the order of a hundred) are preferred due to their ability to achieve fast signal acquisition. Additionally, the relatively high signal-to-noise ratio (SNR) in LEO systems reduces the need for longer spreading codes to mitigate inter-channel interference. In this work, we propose a two-stage block coordinate descent (BCD) method which optimizes the codes' correlation properties while enforcing the autocorrelation sidelobe zero (ACZ) property. In each iteration of the BCD method, we solve a mixed-integer convex program (MICP) over a block of 25 binary variables. Our method is applicable to spreading code families of arbitrary sizes and lengths, and we demonstrate its effectiveness for a problem with 66 length-127 codes and a problem with 130 length-257 codes.

eess.SP↗

arXiv2024

gnss_lib_py: Analyzing GNSS Data with Python

This paper presents gnss_lib_py, a Python library used to parse, analyze, and visualize data from a variety of GNSS (Global Navigation Satellite Systems) data sources. The gnss_lib_py library's ease of use, modular capabilities, testing coverage, and extensive documentation make it an attractive tool not only for scientific and industry users wanting a quick, out-of-the-box solution but also for advanced GNSS users developing new GNSS algorithms. gnss_lib_py has already demonstrated its usefulness and impact through presentation in academic conferences, use in research papers, and adoption in graduate-level university course curricula.

cs.RO↗

arXiv2024

A Survey of Machine Learning Techniques for Improving Global Navigation Satellite Systems

Global Navigation Satellite Systems (GNSS)-based positioning plays a crucial role in various applications, including navigation, transportation, logistics, mapping, and emergency services. Traditional GNSS positioning methods are model-based and they utilize satellite geometry and the known properties of satellite signals. However, model-based methods have limitations in challenging environments and often lack adaptability to uncertain noise models. This paper highlights recent advances in Machine Learning (ML) and its potential to address these limitations. It covers a broad range of ML methods, including supervised learning, unsupervised learning, deep learning, and hybrid approaches. The survey provides insights into positioning applications related to GNSS such as signal analysis, anomaly detection, multi-sensor integration, prediction, and accuracy enhancement using ML. It discusses the strengths, limitations, and challenges of current ML-based approaches for GNSS positioning, providing a comprehensive overview of the field.

eess.SP↗