Semantic segmentation of large-scale outdoor point cloud captured by light detection and ranging (LiDAR) sensors can provide fine-grain and stereoscopic comprehension for the surrounding environment. However, limited by the receptive field of convolution kernel and ignoration of specific spatial properties inherent to the large-scale outdoor point cloud, the existing advanced LiDAR semantic segmentation methods inevitably abandon the unique radial long-range topological relationships. To this end, from the LiDAR perspective, we propose a novel Radial Transformer that can naturally and efficiently exploit the radial long-range dependencies exclusive to the outdoor point cloud for accurate LiDAR semantic segmentation. Specifically, we first develop a radial window partition to generate a series of candidate point sequences and then construct the long-range interactions among the densely continuous point sequences by the self-attention mechanism. Moreover, considering the varying-distance distribution of point cloud in 3-D space, a spatial-adaptive position encoding is particularly designed to elaborate the relative position. Furthermore, we fusion radial balanced attention for a better structure representation of real-world scenes and distant points. Extensive experiments demonstrate the effectiveness and superiority of our method, which achieves 67.5% and 77.7% mean intersection-over-union (mIoU) on two recognized large-scale outdoor LiDAR point cloud datasets SemanticKITTI and nuScenes, respectively.

Light detection and ranging (LiDAR) positioning has received great attention especially when satellites fail. However, the positioning accuracy is still subjected to the following. First, the LiDAR positioning accuracy is affected by the sparsity due to LiDAR beams and environment. Second, the existing methods for suppressing cumulative errors are based on ego-vehicle sensing that requires the motion of repeated paths. Moreover, the output frequency of LiDAR is low. To solve the above problems, an enhanced-LiDAR/ultrawideband (UWB)/inertial navigation system integrated positioning methodology is proposed. First, the enhanced LiDAR odometry module is designed to improve the resolution of LiDAR beams for more accurate odometry. Then, the cooperative optimization module is proposed to introduce UWB observation to suppress the accumulated error without relying on ego-vehicle sensing. Finally, the factor graph fusion module is used to fuse multisensor information dynamically and improve the output frequency. Experimental results prove the effectiveness of our methodology.

In large-scale long-term dynamic environments, high-frequency dynamic objects inevitably lead to significant changes in the appearance of the scene at the same location at different times, which is catastrophic for place recognition (PR). Therefore, how to eliminate the influence of dynamic objects to achieve robust PR has universal practical value for mobile robots and autonomous vehicles. To this end, we suggest a novel semantically consistent LiDAR PR method based on chained cascade network, called SC_LPR, which mainly consists of a LiDAR semantic image inpainting network (LSI-Net) and a semantic pyramid Transformer-based PR network (SPT-Net). Specifically, LSI-Net is a coarse-to-fine generative adversarial network (GAN) with a gated convolutional autoencoder as the backbone. To effectively address the challenges posed by variable-scale dynamic object masks, we integrate the updated Transformer block with mask attention and gated trident block into LSI-Net. Sequentially, in order to generate a discriminative global descriptor representing the point cloud, we design an encoder with pyramid Transformer block to efficiently encode long-range dependencies and global contexts between different categories in the inpainted semantic image, followed by an augmented NetVALD, a generalized VLAD (Vector of Locally Aggregated Descriptors) layer that adaptively aggregates salient local features. Last but not least, we first attempt to create a LiDAR semantic inpainting dataset, called LSI-Dataset, to effectively validate the proposed method. Experimental comparisons show that our method not only improves semantic inpainting performance by about 6%, but also improves PR performance in dynamic environments by about 8% compared to the representative optimal baseline. LSI-Dataset will be publicly available at https://github.KD.LPR.com/.