## 2 Materials and Methods  
### The Problem and Why We Need Framework  
Multiagent systems are most effective in diverse, dynamic, or even chaotic environments. A successful example is [2], but a more rigid communication architecture would require complex setup and adjustment of the communication method when introducing a new type of agent.  
Consider a scenario involving a rescue robot capable of removing rubble and a drone surveying the environment to find survivors and provide a path for the other agent. This real-world problem highlights the benefits of multiagent systems and the challenges encountered with traditional SLAM (simultaneous localisation and mapping) methods. The issues identified in the previous section are informative in this context.  
The first problem arises when the drone maps a \"safe\" path for the ground agent but the latter cannot locate the same landmarks due to object obfuscation, sensor noise, or bad orientation. The dynamic and chaotic nature of the environment exacerbates this issue, as the ground agent may not observe the landmark correctly, or the landmark may no longer exist.  
This problem is further compounded by the fact that the state space in SLAM is fully correlated to robot pose and environment landmarks [7], leading to issues with full observability  
To the best of our knowledge, there has not been a direct comparison of our approach presented in this paper with existing methods. Therefore, to provide a fair evaluation of our contribution, we include a summary. The summary is of recent work to identify areas where our approach offers novelty and improvement. Table 1 lists recent contributions in the relevant fields and the challenges they faced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Methodology** & **Key Findings** & **Challenges** \\ \hline A Versatile and Efficient & & Outperformed state-of-the-art & Difficulty in handling \\ Stereo Visual Inertial SLAM & Stereo visual-inertial SLAM & SLAM systems on several & featureless environments and \\ Stereo Visual [8] & system & datasets while being & robustness to \\  & & computationally efficient. & lighting changes. \\ \hline ORB-SLAM3: An Accurate & & & \\ Open-Source Library for & & Visual–inertial, & \\ Visual, Visual–inertial, & and multi-map SLAM system. & & \\  & & & \\ and Multi-Map SLAM [9] & & & \\ \hline Real-time LiDAR Odometry & LiDAR odometry and & Improved accuracy of LiDAR & Difficulty in handling \\ and Mapping on Variable & mapping using surface & odometry and mapping on & dynamic environments and \\ Terrain using Surface Normal & normal voxel representation. & variable terrain compared to & varying \\ Voxel Representation [10] & & traditional approaches. & LiDAR configurations. \\ A Hierarchical Point Cloud & Hierarchical voxel-based & Improved accuracy and & Limited scalability to \\ Registration Method Based on & point cloud registration & robustness of point cloud & large-scale point clouds and \\ Coarse-to-Fine & method. & registration compared to & difficulty in handling \\ Voxelisation [11] & & existing methods. & featureless areas. \\ \hline  & & Achieved state-of-the-art & \\  & & & \\ Voxel Map for Visual & & & \\ SLAM [12] & monocular visual-inertial & & \\  & & & \\ SLAM system. & & robustness to camera motion & \\  & & & \\  & & & \\ \hline  & & The proposed voxel-based & \\  & & representation learning & \\  & & method is effective for place & \\  & & recognition using 3D point & Limited to place recognition \\  & & clouds. The method & tasks and may not be suitable \\  & & & outperforms state-of-the art & for other applications that \\ Learning for Place & & methods in terms of accuracy & require a more detailed \\ Recognition Based on 3D & voxel