SSC: Semantic Scan Context for Large-Scale Place Recognition

07/01/2021
by   Lin Li, et al.
0

Place recognition gives a SLAM system the ability to correct cumulative errors. Unlike images that contain rich texture features, point clouds are almost pure geometric information which makes place recognition based on point clouds challenging. Existing works usually encode low-level features such as coordinate, normal, reflection intensity, etc., as local or global descriptors to represent scenes. Besides, they often ignore the translation between point clouds when matching descriptors. Different from most existing methods, we explore the use of high-level features, namely semantics, to improve the descriptor's representation ability. Also, when matching descriptors, we try to correct the translation between point clouds to improve accuracy. Concretely, we propose a novel global descriptor, Semantic Scan Context, which explores semantic information to represent scenes more effectively. We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance. Our experiments on the KITTI dataset show that our approach outperforms the state-of-the-art methods with a large margin. Our code is available at: https://github.com/lilin-hitcrt/SSC.

READ FULL TEXT

page 1

page 2

research
08/26/2020

Semantic Graph Based Place Recognition for 3D Point Clouds

Due to the difficulty in generating the effective descriptors which are ...
research
09/26/2022

STD: Stable Triangle Descriptor for 3D place recognition

In this work, we present a novel global descriptor termed stable triangl...
research
11/27/2021

DSC: Deep Scan Context Descriptor for Large-Scale Place Recognition

LiDAR-based place recognition is an essential and challenging task both ...
research
06/07/2022

Object Scan Context: Object-centric Spatial Descriptor for Place Recognition within 3D Point Cloud Map

Place recognition technology endows a SLAM algorithm with the ability to...
research
04/25/2018

SegMap: 3D Segment Mapping using Data-Driven Descriptors

When performing localization and mapping, working at the level of struct...
research
09/01/2020

Distinctive 3D local deep descriptors

We present a simple but yet effective method for learning distinctive 3D...
research
02/07/2018

PPFNet: Global Context Aware Local Features for Robust 3D Point Matching

We present PPFNet - Point Pair Feature NETwork for deeply learning a glo...

Please sign up or login with your details

Forgot password? Click here to reset