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Daniil Lisus

7 accepted papers

2026

Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization

RSS 2026poster

This paper introduces Dr-BA, a first-of-its-kind radar bundle adjustment (BA) framework that operates directly on 2D spinning radar intensity images. Unlike camera or lidar sensors, radar is largely unaffected by precipitation, making it a critical modality for autonomous systems that require all-we…

Cited by 0SourceScholar
2025

Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?

RA-L 2025

Spinning, frequency-modulated continuous-wave (FMCW) radars with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360 ^{\circ }$</tex-math></inline-formula> coverage have been gaining popularity for autonomous-vehic

Cited by 14SourceScholar
2025

DRO: Doppler-Aware Direct Radar Odometry with Gyroscope

RSS 2025poster

A renaissance in radar-based sensing for mobile robotic applications is underway. Compared to cameras or lidars, millimetre-wave radars have the ability to `see’ through thin walls, vegetation, and adversarial weather conditions such as heavy rain, fog, snow, and dust. In this paper, we propose a no…

Cited by 0PDFScholar
2025

Prepared for the Worst: Resilience Analysis of the ICP Algorithm via Learning-Based Worst-Case Adversarial Attacks

ICRA 2025

This paper presents a novel method for assessing the resilience of the iterative closest point (ICP) algorithm via learning-based, worst-case attacks on lidar point clouds. For safety-critical applications such as autonomous navigation, ensuring the resilience of algorithms before deployments is cru

Cited by 4SourceScholar
2024

Toward Certifying Maps for Safe Registration-Based Localization Under Adverse Conditions

RA-L 2024

In this letter, we propose a way to model the resilience of the Iterative Closest Point (ICP) algorithm in the presence of corrupted measurements. In the context of autonomous vehicles, certifying the safety of the localization process poses a significant challenge. As robots evolve in a complex wor

Cited by 7SourceScholar
2023

Know What You Don't Know: Consistency in Sliding Window Filtering With Unobservable States Applied to Visual-Inertial SLAM

RA-L 2023

Estimation algorithms, such as the sliding window filter, produce an estimate and uncertainty of desired states. This task becomes challenging when the problem involves unobservable states. In these situations, it is critical for the algorithm to “know what it doesn't know”, meaning that it must mai

Cited by 9SourceScholar
2021

Heading Estimation Using Ultra-Wideband Received Signal Strength and Gaussian Processes

RA-L 2021

It is essential that a robot has the ability to determine its position and orientation to execute tasks autonomously. Heading estimation is especially challenging in indoor environments where magnetic distortions make magnetometer-based heading estimation difficult. Ultra-wideband (UWB) transceivers

Cited by 1SourceScholar