# Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment

Source: [Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment](<https://www.sota2.com/research/paper/68fa66e81e209131cea13169>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:01:21.007Z

Authors: Zirui Zhao, Yijun Mao, Yan Ding, Pengju Ren, Nanning Zheng

Published: 2020-01-04T03:34:23.000Z

## Abstract

Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction. In this paper, we built a system to creat a semantic 3D map by combining 3D point cloud from ORB SLAM with semantic segmentation information from Convolutional Neural Network model PSPNet-101 for large-scale environments. Besides, a new dataset for KITTI sequences has been built, which contains the GPS information and labels of landmarks from Google Map in related streets of the sequences. Moreover, we find a way to associate the real-world landmark with point cloud map and built a topological map based on semantic map.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2001.01028>)

[PDF](<https://arxiv.org/pdf/2001.01028.pdf>)