¶ autonomous SLAM-based LiDAR mapping
| Company | Emesent |
| Country | Australia |
| Website | https://emesent.com/ |
| Category | Planning & Inventory › Remote sensing & mapping |
¶ Overview
Autonomous SLAM-based LiDAR mapping in forestry refers to the use of Simultaneous Localisation and Mapping (SLAM) algorithms processing data from Light Detection and Ranging (LiDAR) sensors to build accurate, high-resolution 3D maps of forest environments in real time, without relying on pre-existing maps or continuous GNSS coverage. SLAM solves a fundamental chicken-and-egg problem in autonomous navigation: a robot needs a map to localise itself, but needs to know its location to build a map — SLAM algorithms, such as Cartographer, LIO-SAM, and LeGO-LOAM, solve both simultaneously by continuously matching incoming LiDAR point-cloud scans against the accumulated map and refining the estimated trajectory. In dense forest environments, where canopy cover typically reduces GPS accuracy to several metres or makes it unavailable entirely, SLAM-based LiDAR mapping is the primary means by which ground robots and low-altitude UAVs maintain positional awareness. Forestry-specific applications include individual tree stem mapping for automated inventory (where SLAM-derived maps allow precise geo-referencing of each stem's diameter and position), route planning for autonomous harvesters and forwarders navigating extraction corridors, and post-harvest compliance mapping to verify that coupe boundaries and riparian exclusion zones have been respected. Terrestrial LiDAR SLAM systems mounted on backpack, handheld, or robot-carried units have been extensively evaluated in Australian forest types including Pinus radiata plantations and mixed eucalyptus forest, demonstrating the ability to map stem positions to sub-10 cm accuracy across multiple hectares in a single traverse. Challenges include SLAM drift over long traverses, degraded scan matching in areas of low feature density such as young even-aged stands, and the computational demands of real-time processing in resource-constrained field deployments. Continued improvements in solid-state LiDAR hardware, faster onboard processors, and learning-based place recognition are progressively resolving these limitations, making autonomous SLAM-based LiDAR mapping a cornerstone technology for the digital forest of the future.
¶ Assessment
A productive tool that accelerates mapping while significantly lowering safety risks by removing crews from dangerous areas.
¶ Details
| Field | Value |
|---|---|
| Company | Emesent |
| Country | Australia |
| Type | Company |
| FWPA RD&E | 7.3 |