Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR Sensors Article Swipe
Himanshu Gupta
,
Henrik Andreasson
,
Achim J. Lilienthal
,
Polina Kurtser
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23104736
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23104736
Automated forest machines are becoming important due to human operators’ complex and dangerous working conditions, leading to a labor shortage. This study proposes a new method for robust SLAM and tree mapping using low-resolution LiDAR sensors in forestry conditions. Our method relies on tree detection to perform scan registration and pose correction using only low-resolution LiDAR sensors (16Ch, 32Ch) or narrow field of view Solid State LiDARs without additional sensory modalities like GPS or IMU. We evaluate our approach on three datasets, including two private and one public dataset, and demonstrate improved navigation accuracy, scan registration, tree localization, and tree diameter estimation compared to current approaches in forestry machine automation. Our results show that the proposed method yields robust scan registration using detected trees, outperforming generalized feature-based registration algorithms like Fast Point Feature Histogram, with an above 3 m reduction in RMSE for the 16Chanel LiDAR sensor. For Solid-State LiDAR the algorithm achieves a similar RMSE of 3.7 m. Additionally, our adaptive pre-processing and heuristic approach to tree detection increased the number of detected trees by 13% compared to the current approach of using fixed radius search parameters for pre-processing. Our automated tree trunk diameter estimation method yields a mean absolute error of 4.3 cm (RSME = 6.5 cm) for the local map and complete trajectory maps.
Related Topics
Concepts
Lidar
Artificial intelligence
Mean squared error
Computer science
Computer vision
Point cloud
Tree (set theory)
Feature (linguistics)
Global Positioning System
Remote sensing
Pattern recognition (psychology)
Geography
Mathematics
Statistics
Philosophy
Telecommunications
Linguistics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23104736
- https://www.mdpi.com/1424-8220/23/10/4736/pdf?version=1684052877
- OA Status
- gold
- Cited By
- 6
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376612281
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4376612281Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23104736Digital Object Identifier
- Title
-
Robust Scan Registration for Navigation in Forest Environment Using Low-Resolution LiDAR SensorsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-14Full publication date if available
- Authors
-
Himanshu Gupta, Henrik Andreasson, Achim J. Lilienthal, Polina KurtserList of authors in order
- Landing page
-
https://doi.org/10.3390/s23104736Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/10/4736/pdf?version=1684052877Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/23/10/4736/pdf?version=1684052877Direct OA link when available
- Concepts
-
Lidar, Artificial intelligence, Mean squared error, Computer science, Computer vision, Point cloud, Tree (set theory), Feature (linguistics), Global Positioning System, Remote sensing, Pattern recognition (psychology), Geography, Mathematics, Statistics, Philosophy, Telecommunications, Linguistics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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