# AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot

arXiv: https://arxiv.org/abs/2007.01813 · HTML: https://mlsanity.com/paper/2007.01813/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 23 of 23)

- [Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection](https://mlsanity.com/paper/2301.02371/) (2023)
- [RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving](https://mlsanity.com/paper/2106.02527/) (2021)
- [ParkingE2E: Camera-based End-to-end Parking Network, from Images to Planning](https://mlsanity.com/paper/2408.02061/) (2024)
- [Semantic Visual Simultaneous Localization and Mapping: A Survey](https://mlsanity.com/paper/2209.06428/) (2022)
- [ParkGaussian: Surround-view 3D Gaussian Splatting for Autonomous Parking](https://mlsanity.com/paper/2601.01386/) (2026)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes](https://mlsanity.com/paper/2107.02557/) (2021)
- [Accurate calibration of multi-perspective cameras from a generalization of the hand-eye constraint](https://mlsanity.com/paper/2202.00886/) (2022)
- [FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow](https://mlsanity.com/paper/2305.01622/) (2023)
- [Learning Interpretable BEV Based VIO without Deep Neural Networks](https://mlsanity.com/paper/2109.12292/) (2021)
- [Parking Spot Classification based on surround view camera system](https://mlsanity.com/paper/2310.12997/) (2023)
- [Keeping Less is More: Point Sparsification for Visual SLAM](https://mlsanity.com/paper/2207.00225/) (2022)
- [Radiance Field Learners As UAV First-Person Viewers](https://mlsanity.com/paper/2408.05533/) (2024)
- [Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images](https://mlsanity.com/paper/2411.12620/) (2024)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [Sparse Semantic Map-Based Monocular Localization in Traffic Scenes Using Learned 2D-3D Point-Line Correspondences](https://mlsanity.com/paper/2210.04543/) (2022)
- [Online Camera-to-ground Calibration for Autonomous Driving](https://mlsanity.com/paper/2303.17137/) (2023)
- [LineMarkNet: Line Landmark Detection for Valet Parking](https://mlsanity.com/paper/2309.10475/) (2023)
- [CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving](https://mlsanity.com/paper/2512.03510/) (2025)
- [ParkingTwin: Training-Free Streaming 3D Reconstruction for Parking-Lot Digital Twins](https://mlsanity.com/paper/2601.13706/) (2026)
- [DMV-AVP: Distributed Multi-Vehicle Autonomous Valet Parking Using Autoware](https://mlsanity.com/paper/2601.16327/) (2026)
- [ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes](https://mlsanity.com/paper/2604.22835/) (2026)
- [ZVeC: A Zero-Shot Framework for Instance-Level Vehicle Extraction and Generative Point Cloud Completion](https://mlsanity.com/paper/2609.24825/) (2026)

## Abstract (arXiv)

Autonomous valet parking is a specific application for autonomous vehicles. In this task, vehicles need to navigate in narrow, crowded and GPS-denied parking lots. Accurate localization ability is of great importance. Traditional visual-based methods suffer from tracking lost due to texture-less regions, repeated structures, and appearance changes. In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots. Semantic features contain guide signs, parking lines, speed bumps, etc, which typically appear in parking lots. Compared with traditional features, these semantic features are long-term stable and robust to the perspective and illumination change. We adopt four surround-view cameras to increase the perception range. Assisting by an IMU (Inertial Measurement Unit) and wheel encoders, the proposed system generates a global visual semantic map. This map is further used to localize vehicles at the centimeter level. We analyze the accuracy and recall of our system and compare it against other methods in real experiments. Furthermore, we demonstrate the practicability of the proposed system by the autonomous parking application.