# Robust Egoistic Rigid Body Localization

arXiv: https://arxiv.org/abs/2501.10219 · HTML: https://mlsanity.com/paper/2501.10219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 57 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 16 of 16)

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- [A Survey on Global LiDAR Localization: Challenges, Advances and Open Problems](https://mlsanity.com/paper/2302.07433/) (2023)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [An Overview of Signal Processing Techniques for Joint Communication and Radar Sensing](https://mlsanity.com/paper/2102.12780/) (2021)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals](https://mlsanity.com/paper/2012.11171/) (2020)
- [Typing linear algebra: A biproduct-oriented approach](https://mlsanity.com/paper/1312.4818/) (2013)
- [Multi Object Tracking for Predictive Collision Avoidance](https://mlsanity.com/paper/2307.02161/) (2023)
- [Global and Asymptotically Efficient Localization from Range Measurements](https://mlsanity.com/paper/2203.16951/) (2022)
- [Soft-connected Rigid Body Localization: State-of-the-Art and Research Directions for 6G](https://mlsanity.com/paper/2309.05002/) (2023)
- [Fast and Efficient Sequential Radar Parameter Estimation in MIMO-OTFS Systems](https://mlsanity.com/paper/2402.14612/) (2024)
- [Joint Channel, Data, and Radar Parameter Estimation for AFDM Systems in Doubly-Dispersive Channels](https://mlsanity.com/paper/2405.16945/) (2024)
- [Belief Propagation-based Rotation and Translation Estimation for Rigid Body Localization](https://mlsanity.com/paper/2407.09232/) (2024)
- [Enabling Next-Generation V2X Perception: Wireless Rigid Body Localization and Tracking](https://mlsanity.com/paper/2408.00349/) (2024)

## Abstract (arXiv)

We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without prior knowledge of the shape of the target, and taking into account the possibility that the available observations are incomplete. Three complementary contributions are then offered for such a scenario. The first is a method to estimate the translation vector between the center point of both rigid bodies, which unlike existing techniques does not require that both objects have the same shape or even the same number of landmark points. This technique is shown to significantly outperform the state-of-the-art (SotA) under complete information, but to be sensitive to data erasures, even when enhanced by matrix completion methods. The second contribution, designed to offer improved performance in the presence of incomplete information, offers a robust alternative to the latter, at the expense of a slight relative loss under complete information. Finally, the third contribution is a scheme for the estimation of the rotation matrix describing the relative orientation of the target rigid body with respect to the primary. Comparisons of the proposed schemes and SotA techniques demonstrate the advantage of the contributed methods in terms of root mean square error (RMSE) performance under fully complete information and incomplete conditions.