AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM

TL;DR

AgriGS-SLAM is a real-time SLAM system for orchards that combines LiDAR and multi-view Gaussian Splatting to produce detailed, stable 3D maps across seasons, improving perception for autonomous agricultural robots.

Contribution

This work introduces a novel SLAM framework integrating direct LiDAR odometry with multi-view Gaussian Splatting, optimized for seasonal orchard mapping with enhanced detail and stability.

Findings

Sharper, more stable reconstructions across seasons.

Steadier trajectories compared to state-of-the-art baselines.

Maintains real-time performance on-tractor.

Abstract

Autonomous robots in orchards require real-time 3D scene understanding despite repetitive row geometry, seasonal appearance changes, and wind-driven foliage motion. We present AgriGS-SLAM, a Visual--LiDAR SLAM framework that couples direct LiDAR odometry and loop closures with multi-camera 3D Gaussian Splatting (3DGS) rendering. Batch rasterization across complementary viewpoints recovers orchard structure under occlusions, while a unified gradient-driven map lifecycle executed between keyframes preserves fine details and bounds memory. Pose refinement is guided by a probabilistic LiDAR-based depth consistency term, back-propagated through the camera projection to tighten geometry-appearance coupling. We deploy the system on a field platform in apple and pear orchards across dormancy, flowering, and harvesting, using a standardized trajectory protocol that evaluates both training-view and novel-view synthesis to reduce 3DGS overfitting in evaluation. Across seasons and sites, AgriGS-SLAM delivers sharper, more stable reconstructions and steadier trajectories than recent state-of-the-art 3DGS-SLAM baselines while maintaining real-time performance on-tractor. While demonstrated in orchard monitoring, the approach can be applied to other outdoor domains requiring robust multimodal perception.