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.

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by Li-DAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a$2.3\times reduction$in model parameters and$64l\times fewer$multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).

We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth estimation for autonomous driving applications. Our driving platform is equipped with a high resolution 128 channel LiDAR, a 2MPix stereo camera, a lux meter and a GNSSlINS system. Ambient and reflectivity images are made available along with the LiDAR point clouds to facilitate multi-modal use of concurrent ambient and reflectivity scene information. Leveraging DurLAR, with a resolution exceeding that of prior benchmarks, we consider the task of monocular depth estimation and use this increased availability of higher resolution, yet sparse ground truth scene depth information to propose a novel joint supervised/self-supervised loss formulation. We compare performance over both our new DurLAR dataset, the established KITTI benchmark and the Cityscapes dataset. Our evaluation shows our joint use supervised and self-supervised loss terms, enabled via the superior ground truth resolution and availability within DurLAR improves the quantitative and qualitative performance of leading contemporary monocular depth estimation approaches (RMSE = 3.639, SqRel = 0.936).