Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Abstract

Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, longterm stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra-as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five realworld multi-robot experiments in areas of up 3,000 m 2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano. K E Y W O R D S graph SLAM, map matching, mobile robots, multi-robot, navigation filter 1 | INTRODUCTION The exploration of moons and foreign planets is an important current and future application for mobile robots as their surfaces are difficult to reach and hard to access for humans. The application of huge and complex robot systems such as Curiosity, landed on Mars in 2012, creates many single points of failure for a mission. As a consequence, these rovers have to move very slowly and carefully to avoid getting stuck, as the Mars rover Spirit did in 2009 (Wolchover, 2011). The future deployment of teams of multiple robots can avoid these single points of failure by gaining robustness through redundancy and, in addition, can improve efficiency through parallelization. The robots have to travel through previously unknown unstructured rough terrain, operating in areas where external methods for localization like global navigation satellite systems (GNSS) are not available or expensive to set up. Communication links to the robots are limited and heavily delayed, featuring for example 8-40 min round trip time between Earth andMars. Furthermore, communication between the robots cannot be guaranteed at all times, in particular at scientifically interesting places such as craters, canyons, or caves. As teleoperation therefore becomes inefficient or infeasible, robot autonomy is a key aspect for future planetary exploration missions. Any coordinated (semi-)autonomous operation in such challenging environments requires up-to-date localization estimates for all robots in a team as well as a joint map to operate on.

ARDEA—An MAV with skills for future planetary missions

“…For the mapping framework we plan to match submaps created by ARDEA to gain further intra‐ and inter‐robot loop closure constraints for relocalization based on the 3D geometry of the environment, indicated by the dashed line in Figure 21. We already implemented and demonstrated such a method for teams of planetary exploration rover prototypes in Schuster et al (2018). In future work, we could also reduce this computational effort by restricting the compositions to application‐dependent requests and limiting them to regions of interest.…”

“…In contrast, we process our stereo pairs decoupled form each other, similar to Beul et al (2015), and fuse their VO results in a real‐time capable filter for local state estimation. Thereby, the complexity of our global graph‐based estimation is not affected by the number of high‐frequency sensors, allowing for fast online optimization steps (Schuster, Schmid, Brand, & Beetz, 2018). The approach described in Oskiper, Zhu, Samarasekera, and Kumar (2007) uses two visual odometries, where only one is selected to be fused with an inertial measurement unit IMU.…”

“…For global localization and mapping, we employ our 6D SLAM framework introduced in Schuster, Brand, Hirschmüller, Suppa, and Beetz (2015, 2018). It enables efficient online and on‐board single‐ and multirobot global localization and mapping by building upon the estimates from the local navigation filter and complementing them with additional intra‐ and inter‐robot loop closure constraints.…”

Search and Rescue Under the Forest Canopy Using Multiple UAS

“…Schmuck and Chli (2018) extend this framework by sending optimized keyframes and map points back to the vehicles to improve accuracy of local mapping. On the other hand, Dong et al (2015), Morrison et al (2016) and Schuster et al (2019) propose to run full SLAM onboard each vehicle. The incurred computation costs are further reduced in distributed architectures, where each robot only optimizes its local map and shares the compressed map or boundary poses with each other, see Cunningham et al (2013); Choudhary et al (2017); Cieslewski et al (2018).…”

“…While dense volumetric maps provide accurate geometric information for path planning, they are not data efficient and hence are not suitable for communication over a lowbandwidth wireless network. Other representations such as 3D point clouds (Schuster et al 2019) or feature-based maps (Schmuck and Chli 2018) can be more lightweight, but both can still result in potentially heavy data payload. To achieve lightweight communication, we turn to object-based representations, more specifically a tree-based representation (Kukko et al 2017).…”