arXiv subjects

Hao Qiao

Publications and source records attributed to Hao Qiao.

6 recordsLinked to original sources

arXiv2026

MoE-Based Learned Inertial Odometry for Bicycle Localization

GNSS suffers from multipath errors in urban canyons, making reliable bicycle localization difficult. Hand-crafted inertial alternatives, such as cycling dead reckoning and nonholonomic constraints, fail to generalize across riders, postures, and road surfaces. This work implements learned inertial odometry for bicycle localization: a neural velocity predictor, trained on multirider, multi-bicycle, and multi-surface IMU data, is tightly coupled with an extended Kalman filter for three-dimensional pose estimation, replacing hand-crafted cycling models with a data-driven representation. To reduce computational overhead, the predictor is realized as a sparsely-gated Mixture of Experts (MoE) network with top-K routing, trained under an alternating parameter-freezing scheme with a per-expert capacity constraint to encourage diverse and balanced specialization.The proposed model attains 0.333 m/s inference error, 9.49 m ATE, and 2.58 m RTE at only 28.76 M FLOPs. This corresponds to approximately 7 times and 9 times fewer FLOPs than the ResMLP and ResNet backbones used in LLIO and TLIO, while maintaining comparable or superior accuracy. The proposed MoE variants further rank first on both ATE and RTE across all evaluation splits, demonstrating strong generalization.

cs.RO↗

arXiv2026

DINS-IO: Learned Inertial Odometry via Differentiable INS Consistency

The training of learned inertial odometry depends on dense, high-precision position ground truth from motion capture, visual-inertial odometry or SLAM, which is costly and hard to acquire at scale. We propose DINS-IO, which learns inertial odometry directly from raw IMU streams without position labels. Our key insight is that the strapdown INS velocity recursion is a strong, fully differentiable consistency prior: the predicted velocity, rotated into the navigation frame, must agree with the integrated specific force up to an unknown initial velocity and a constant accelerometer bias. We cast this constraint as a sliding-window least-squares problem with a globally shared bias, solve it in closed form, and use the solver residual as a self-supervised loss whose gradient flows back to the network through the analytic solution. To supply this per-sample constraint, we design a high-frequency network that emits dense body-frame velocity at the IMU rate. Since the self-supervised network learns consistent motion but its velocity is not yet metrically calibrated, we calibrate it to true metric velocity from a few labeled trajectories by directly supervising the predicted body-frame velocity and adapting only low-rank (LoRA) patches. On standard benchmarks, DINS-IO pretrained self-supervised and fine-tuned with a small fraction of labels matches or surpasses fully supervised baselines.

cs.RO↗

arXiv2021

BambooMC -- A Geant4-based simulation program for the PandaX experiments

The purpose of the PandaX experiments is to search for the possible events resulted from dark matter particles, neutrinoless double beta decay or other rare processes with xenon detectors. Understanding the energy depositions from backgrounds or calibration sources in these detectors is very important. The program of BambooMC is created to perform the Geant4-based Monte Carlo simulation, providing reference information for the experiments. We introduce the design and features of BambooMC in this report. The running of the program depends on a configuration file, which combines different detectors, event generators, physics lists and analysis packs together in one simulation. The program can be easily extended and applied to other experiments.

physics.ins-det↗

arXiv2019

Design and commissioning of a 600 L Time Projection Chamber with Microbulk Micromegas

We report the design, construction, and initial commissioning results of a large high pressure gaseous Time Projection Chamber (TPC) with Micromegas modules for charge readout. The detector vessel has an inner volume of about 600 L and an active volume of 270 L. At 10 bar operating pressure, the active volume contains about 20 kg of xenon gas and can image charged particle tracks. Drift electrons are collected by the charge readout plane, which accommodates a tessellation of seven Micromegas modules. Each of the Micromegas covers a square of 20 cm by 20 cm. A new type of Microbulk Micromegas is chosen for this application due to its good gain uniformity and low radioactive contamination. Initial commissioning results with 1 Micromegas module running with 1 bar argon and isobutane gas mixture and 5 bar xenon and trimethylamine (TMA) gas mixture are reported. We also recorded extended background tracks from cosmic ray events and highlighted the unique tracking feature of this gaseous TPC.

physics.ins-det↗

arXiv2018

Signal-background discrimination with convolutional neural networks in the PandaX-III experiment using MC simulation

The PandaX-III experiment will search for neutrinoless double beta decay of $^{136}$Xe with high pressure gaseous time projection chambers at the China Jin-Ping underground Laboratory. The tracking feature of gaseous detectors helps suppress the background level, resulting in the improvement of the detection sensitivity. We study a method based on the convolutional neural networks to discriminate double beta decay signals against the background from high energy gammas generated by $^{214}$Bi and $^{208}$Tl decays based on detailed Monte Carlo simulation. Using the 2-dimensional projections of recorded tracks on two planes, the method successfully suppresses the background level by a factor larger than 100 with a high signal efficiency. An improvement of $62\%$ on the efficiency ratio of $ε_{s}/\sqrt{ε_{b}}$ is achieved in comparison with the baseline in the PandaX-III conceptual design report.

physics.ins-det↗

arXiv2016

PandaX-III: Searching for Neutrinoless Double Beta Decay with High Pressure $^{136}$Xe Gas Time Projection Chambers

Searching for the Neutrinoless Double Beta Decay (NLDBD) is now regarded as the topmost promising technique to explore the nature of neutrinos after the discovery of neutrino masses in oscillation experiments. PandaX-III (Particle And Astrophysical Xenon Experiment III) will search for the NLDBD of $^{136}$Xe at the China Jin Ping underground Laboratory (CJPL). In the first phase of the experiment, a high pressure gas Time Projection Chamber (TPC) will contain 200 kg, 90% $^{136}$Xe enriched gas operated at 10 bar. Fine pitch micro-pattern gas detector (Microbulk Micromegas) will be used at both ends of the TPC for the charge readout with a cathode in the middle. Charge signals can be used to reconstruct tracks of NLDBD events and provide good energy and spatial resolution. The detector will be immersed in a large water tank to ensure $\sim$5 m of water shielding in all directions. The second phase, a ton-scale experiment, will consist of five TPCs in the same water tank, with improved energy resolution and better control over backgrounds.

physics.ins-det↗