We present the first cross-modality distillation framework specifically tailored for single-panoramic-camera Bird’s-Eye-View (BEV) segmentation. Our approach leverages a novel LiDAR image representation fused from range, intensity and ambient channels, together with a voxel-aligned view transformer that preserves spatial fidelity while enabling efficient BEV processing. During training, a high-capacity LiDAR and camera fusion Teacher network extracts both rich spatial and semantic features for cross-modality knowledge distillation into a lightweight Student network that relies solely on a single 360-degree panoramic camera image. Extensive experiments on the Dur360BEV dataset demonstrate that our teacher model significantly outperforms existing camera-based BEV segmentation methods, achieving a 25.6% IoU improvement. Meanwhile, the distilled Student network attains competitive performance with an 8.5% IoU gain and state-of-the-art inference speed of 31.2 FPS. Moreover, evaluations on KITTI-360 (two fisheye cameras) confirm that our distillation framework generalises to diverse camera setups, underscoring its feasibility and robustness. This approach reduces sensor complexity and deployment costs while providing a practical solution for efficient, low-cost BEV segmentation in real-world autonomous driving. The code is available at: https://github.com/Tom-E-Durham/KD360-VoxelBEV.

We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a benchmark architecture designed to generate Bird-Eye-View (BEV) maps using only a single spherical camera. This dataset and benchmark address the challenges of BEV generation in autonomous driving, particularly by reducing hardware complexity through the use of a single 360-degree camera instead of multiple perspective cameras. Within our benchmark architecture, we propose a novel spherical-image-to-BEV module that leverages spherical imagery and a refined sampling strategy to project features from 2D to 3D. Our approach also includes an innovative application of focal loss, specifically adapted to address the extreme class imbalance often encountered in BEV segmentation tasks, that demonstrates improved segmentation performance on the Dur360BEV dataset. The results show that our benchmark not only simplifies the sensor setup but also achieves competitive performance. Code + Dataset: https://github.com/Tom-E-DurhamJDur360BEV

Detecting traversable pathways in unstructured out-door environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area search and rescue, as well as in incident management scenarios such as forest fires. Current datasets and models primarily focus on either urban environments or wide vehicle-traversable off-road tracks, leaving a substantial gap in tackling the complexities of trail-based off-road scenarios. To address this issue, we introduce the Trail-based Off-road Multimodal Dataset (TOMD), a comprehensive dataset explicitly designed for narrow and unstructured trail-like environments. Our dataset features high-fidelity multimodal sensor data — including 128-channel LiDAR, stereo imagery, GNSS, IMU, and illumination measurements — collected through repeated runs across di-verse environmental conditions. In addition, we propose a novel dynamic multiscale data fusion model for precise traversable pathway prediction in trail-like areas. The study investigates the impact of various fusion processes — early, cross, and mixed — on model performance under different illumination levels: low-light, normal ambient lighting, and bright conditions. The results highlight the effectiveness of our approach, variation in performance across illumination levels, and the potential applicability of the dataset in diverse environmental conditions.Our work provides a valuable resource for advancing trail-based off-road navigation, and we openly publish our TOMD at https://github.com/yyyxs1125/TMOD to establish a future bench-mark in this research domain.