Multi-modal fusion of sensors is a commonly used approach to enhance the performance of odometry estimation, which is also a fundamental module for mobile robots. However, the question of \textit{how to perform fusion among different modalities in a supervised sensor fusion odometry estimation task?} is still one of challenging issues remains. Some simple operations, such as element-wise summation and concatenation, are not capable of assigning adaptive attentional weights to incorporate different modalities efficiently, which make it difficult to achieve competitive odometry results. Recently, the Transformer architecture has shown potential for multi-modal fusion tasks, particularly in the domains of vision with language. In this work, we propose an end-to-end supervised Transformer-based LiDAR-Inertial fusion framework (namely TransFusionOdom) for odometry estimation. The multi-attention fusion module demonstrates different fusion approaches for homogeneous and heterogeneous modalities to address the overfitting problem that can arise from blindly increasing the complexity of the model. Additionally, to interpret the learning process of the Transformer-based multi-modal interactions, a general visualization approach is introduced to illustrate the interactions between modalities. Moreover, exhaustive ablation studies evaluate different multi-modal fusion strategies to verify the performance of the proposed fusion strategy. A synthetic multi-modal dataset is made public to validate the generalization ability of the proposed fusion strategy, which also works for other combinations of different modalities. The quantitative and qualitative odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom could achieve superior performance compared with other related works.

翻译：多模态传感器融合是提升里程计估计性能的常用方法，而里程计估计本身也是移动机器人的基础模块。然而，“在有监督的传感器融合里程计估计任务中，如何实现不同模态之间的融合？”这一问题仍是亟待解决的难点。简单的操作（如逐元素求和与拼接）无法通过分配自适应注意力权重来有效整合不同模态信息，导致难以获得具有竞争力的里程计结果。近年来，Transformer架构在多模态融合任务中展现出潜力，尤其是在视觉与语言领域。本文提出一种端到端的有监督Transformer激光雷达-惯性融合框架（TransFusionOdom）用于里程计估计。多注意力融合模块针对同质和异质模态采用不同的融合策略，以解决盲目增加模型复杂度可能引发的过拟合问题。此外，为解释基于Transformer的多模态交互学习过程，本文引入一种通用可视化方法以呈现模态间的交互机制。通过详尽的消融实验评估多种多模态融合策略，验证所提融合策略的性能。公开合成多模态数据集以验证所提融合策略的泛化能力，该策略同样适用于其他模态组合。基于KITTI数据集的定量与定性里程计评估表明，所提出的TransFusionOdom性能优于相关已有方法。