In this section we provide additional material to complement the paper.
+- Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in - mapping unknown environments while simultaneously estimating their own positions. This approach enhances - robustness, scalability, and accuracy by sharing information between agents, reducing drift, and enabling - collective exploration of larger areas. In this paper, we present Decentralized Visual Monocular SLAM - (DVM-SLAM), the first open-source decentralized monocular C-SLAM system. By only utilizing low-cost and - light-weight monocular vision sensors, our system is well suited for small robots and micro aerial - vehicles (MAVs). DVM-SLAM's real-world applicability is validated on physical robots with a custom - collision avoidance framework, showcasing its potential in real-time multi-agent autonomous navigation - scenarios. We also demonstrate comparable accuracy to state-of-the-art centralized monocular C-SLAM - systems. +
+ In this video, we deploy DVM-SLAM on the Cambridge RoboMaster platform with a custom collision avoidance + framework and test the system in an intersection environment, where the two robots would normally + collide. + The agents are able to localize each other even when their views do not overlap and they can not see + each + other, + demonstrating that a shared map is being built. Out of the four consecutive trials run in this + environment, there were zero collisions between the two agents, and the distance between agents never + went + below the collision threshold of 0.55 meters. The RMS ATE of the system was 7.4cm over the 50-meter-long + trajectory.
+ DVM-SLAM performs incremental, asynchronous, and distributed pose graph optimization through a + keyframe + sharing method. This refines the map as the agents explore the environment, reducing drift and improving + accuracy. +
++ (a) External keyframe $k_{ext}$ with $M_{ext}=\{m_3, m_4, m_5\}$. Note the references to existing + keyframes + and map points. +
++ (b) Existing local map. +
++ (c) Step 1: Move $k_{ext}$ and $M_{ext}$ to the local map and relink references. Relinked + connections are drawn in purple. +
+ (d) Step 2: Merge duplicate map points. $m_5$ and $m_0$ have a similar feature descriptor and + location, + therefore they are merged. +
+ (e) Step 3: Local pose graph optimization around $k_{ext}$ to refine the map using the new + information. + Original keyframe and map point locations are shown in purple. +
++ Visual overview of inserting external keyframes and map points into the local map. External keyframe (a) + and + initial local map (b) are combined to create our final local map (e). This is performed incrementally by + each agent as additional external data is received, and enables to agents to collaboratively contribute to + and benefit from a shared, continuously improving map without the need for centralized coordination. +
BibTex Code Here