Articulated Inter-robot 6D Pose Estimation for Planetary Multi-agent SLAM

Abstract

Planetary exploration robot teams rely on robust exploration pipelines, such as multi-agent SLAM, to perform complex collaborative tasks. Multi-agent SLAM methods rely on accurate relative transformations between agents to collaboratively merge maps, predominantly using indirect associations between keyframes to compute transforms. Recently, direct association methods that use onboard perception modules with pose estimation pipelines have been shown to provide more accurate, long-range relative transformations. However, current methods consider the robot as a single static rigid body and struggle greatly during dynamic movements, complex robot morphologies, and under harsh environmental occlusions. As a potential solution, we represent the robot as an ensemble of articulated components and investigate the use of articulated pose estimation methods with RGB images and intrinsic measurements. Specifically, we introduce novel post-processing steps based on factor graphs and differentiable rendering to refine upstream poses. First, pose estimates of individual components are visually refined using differentiable rendering and a segmentation mask to ensure visual plausibility. Second, by using a factor-graph formulation, we fuse estimated poses with partially available intrinsic measurements to preserve relative robot constraints and reconstruct missing estimates under occlusions. Third, we extend the factor-graph refinement to estimate over multiple frames and use visual odometry data (VO) to mitigate rotational symmetry issues. Experimental results on synthetic planetary terrains demonstrate that our method outperforms single-body pose estimation with a decrease in translational and rotational error by 55% and 61%, respectively. Additionally, our novel factor-graph refinement method is able to recover undetected articulated components under heavy occlusions and provides robust performance under dynamic movements in indoor lab data