# Lidar based 3D Tracking and State Estimation of Dynamic Objects

arXiv: https://arxiv.org/abs/2304.01396 · HTML: https://mlsanity.com/paper/2304.01396/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Vision-RADAR fusion for Robotics BEV Detections: A Survey](https://mlsanity.com/paper/2302.06643/) (2023)
- [Surround-View Vision-based 3D Detection for Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.06650/) (2023)
- [Transformer-Based Sensor Fusion for Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.11481/) (2023)

## Abstract (arXiv)

State estimation of oncoming vehicles: Earlier research has been based on determining states like position, velocity, orientation , angular velocity, etc of ego-vehicle. Our approach focuses on estimating the states of non-ego vehicles which is crucial for Motion planning and decision-making. Dynamic Scene Based Localization: Our project will work on dynamic scenes like moving ego (self) and non-ego vehicles. Previous methods were focused on static environments.