LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gaussians to avoid local minima and ensure global consistency; (2) a Tracking and Alignment Module leveraging learned 3D priors, which combines correspondence-guided PnP initialization with photometric refinement for accurate camera tracking; and (3) an adaptive Octree Anchor Formation mechanism that dynamically adjusts anchor densities, significantly reducing memory usage. Extensive experiments on challenging benchmarks (Tanks and Temples, Free, and Hike datasets) demonstrate that LongSplat achieves state-of-the-art results, substantially improving rendering quality, pose accuracy, and computational efficiency compared to prior approaches.