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Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
Siddharth Choudhary, Luca Carlone, Carlos Nieto, John Rogers, Henrik I. Christensen, Frank Dellaert
TL;DR
The paper studies collaborative mapping by robot teams that lack reliable communication infrastructure and face bandwidth and privacy constraints. It develops distributed pose-graph solvers and object-based mapping, finding that DGS performs well and that the combined approach can reduce communication requirements by several orders of magnitude. The evaluation covers Gazebo simulations and field tests.
Problem
Robot teams need to build maps collaboratively while communicating only limited information in unknown environments, making centralized estimation unsuitable for the targeted constraints.
Method
The paper distributes a two-stage chordal pose-graph optimization using JOR and SOR solvers, extends it to object-based landmarks, and evaluates it in simulations and field tests.
Results
The Distributed Gauss-Seidel method shows accurate, scalable, noise-robust performance with minimal information exchange, while object-based mapping reduces communication requirements by several orders of magnitude in field tests.
Takeaways & Limitations
Distributed Gauss-Seidel with object-based models supports distributed mapping by reducing exchanged information and memory requirements while remaining effective for large robot teams.
Takeaways & Limitations
The evaluation has demonstrated effectiveness in large teams of ground robots, while extension to flying robots remains future work.
Abstract
from arXiv · showhide
We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of the two-stage approach of Carlone et al., where we use the Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as workhorses to split the computation among the robots. As a second contribution, we extend %and demonstrate the applicability of the proposed distributed algorithms to work with object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g., point clouds) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS requires minimal information exchange, has an anytime flavor, scales well to large teams, is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.
1 Introduction
The paper addresses distributed trajectory estimation for robot teams operating under limited communication and privacy constraints. It proposes distributed pose-graph algorithms and object-based mapping, with experiments highlighting DGS and substantial communication savings.
- 1 Introduction: Distributed mapping is designed for teams that communicate only during rendezvous and must minimize exchanged trajectory information under bandwidth and privacy constraints.The setting targets large teams operating in unknown environments, including simulations with up to 50 robots and field experiments with up to 4 robots.
- 1 Introduction: The distributed back-end solves the two quadratic subproblems of chordal initialization using Jacobi Over-Relaxation and Successive Over-Relaxation solvers.The resulting communication burden is linear in the number of robot rendezvous, and the methods do not require an accurate initial guess.
- 1 Introduction: Object-based models reduce information exchange, produce compact human-understandable maps, and lower computational complexity relative to feature-based mapping.They replace low-level point and line representations that can be memory-intensive and redundant.
- 1 Introduction: The Distributed Gauss-Seidel method provides accurate, communication-parsimonious, noise-robust estimates that scale to large robot teams.The paper evaluates the algorithms in realistic Gazebo simulations and field tests at a military facility.
- 1 Introduction: Field tests show that combining distributed algorithms with object-based models reduces communication requirements by several orders of magnitude and enables mapping with large robot teams.The paper connects trajectory estimation to distributed 3D mapping and localization.
2 Related Work
Prior work on multi-robot mapping spans distributed estimation, parallel and hierarchical optimization, distributed localization, and high-level object-based representations. These approaches address communication, scalability, robustness, and semantic modeling, but Gaussian-elimination methods incur dense communication and rely on linearization points.
- Multi Robot Localization and Mapping: Distributed multi-robot estimation addresses communication constraints, heterogeneous teams, estimation consistency, and robust data association through several estimation techniques.Centralized alternatives collect measurements at one inference engine, while variants exchange partial raw or preprocessed sensor data.
- Multi Robot Localization and Mapping: Centralized inference is impractical in hostile or bandwidth-limited settings because one attack can threaten the team, communication is massive, and large problems may exceed one unit’s computation.These concerns motivate distributed approaches to trajectory estimation and mapping.
- Parallel and Hierarchical Approaches for Mapping: Parallel and hierarchical mapping methods use Gaussian elimination, Schur complements, submaps, nested dissection, and divide-and-conquer updates for large-scale problems.The cited approaches reduce dependencies or decompose mapping into hierarchical or sequential subproblems.
- Parallel and Hierarchical Approaches for Mapping: Gaussian-elimination approaches exchange dense marginals with communication cost quadratic in the number of separators and require good linearization points for their linearized problems.These shortcomings motivate sparsification and contribute to implementation complexity.
- Related Work in Other Communities: Distributed localization research in sensor networks, computer vision, and multi-agent systems estimates agent states from relative measurements such as positions or orientations.This literature provides a broader context for distributed position and orientation estimation.
- High-Level Map Representations: High-level mapping represents places, surfaces, regions, and objects semantically, including spatial hierarchies, generative object models, planar landmarks, and learned domain-specific object detectors.These representations support mapping with semantic scene components rather than only low-level measurements.
3 Dealing with Bandwidth Constraints I: Distributed Algorithms
The paper distributes a two-stage pose-graph optimization pipeline across robots, using local measurements and limited rendezvous communication. Distributed JOR and SOR solvers preserve convergence properties while requiring only estimates of poses involved in inter-robot measurements.
- Distributed algorithms enable robots to reach consensus on a globally optimal trajectory estimate while exchanging minimal information.
- Problem formulation: The pose-graph problem uses intra-robot and inter-robot relative pose measurements, with trajectories represented as sequences of 3D poses in SE(3).
- Two-stage optimization: The two-stage approach first relaxes rotation constraints and then recovers full poses with a single Gauss-Newton iteration after projecting rotations onto SO(3).The centralized approach solves two linear systems and does not require an initial guess.
- Distributed solvers: Distributed JOR and SOR split the linear systems by robot, exposing local block equations that support iterative updates using neighboring trajectory estimates.The block equations form the basis for both over-relaxation methods.
- Distributed SOR: SOR converges from any initial estimate exactly when γ ∈ (0, 2), whereas choices outside this interval do not generally converge to the correct solution.The experiments report fastest convergence for γ = 1.
- Communication and initialization: Each robot needs estimates only for poses involved in its inter-robot measurements, so other robots need not reveal their complete trajectories.When γ = 1, distributed SOR is the distributed Gauss-Seidel method; flagged initialization makes all robots initialized after the first iteration.
4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations
The section presents object-based multi-robot SLAM as a bandwidth-conscious alternative to feature-based mapping, using shared object landmarks and distributed estimation. Robots exchange object labels and poses during rendezvous, incorporate intra- and inter-robot factors, and solve the resulting maximum-likelihood problem with Distributed Gauss-Seidel.
- 4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations: Object-based models reduce memory, redundancy, and communication compared with feature-based maps while adding semantic information for tasks such as manipulation and human-robot interaction.The paper uses high-level object models both at the estimation front-end and as the map representation.
- 4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations: Each robot estimates its trajectory and observed object poses, with odometry and object observations forming intra-robot factors and shared object observations forming inter-robot factors.During rendezvous, robots share object labels and poses; matched landmarks create inter-robot measurements.
- 4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations: The object-based maximum-likelihood problem has the same structure as distributed pose graph optimization and is solved using the Distributed Gauss-Seidel method.Robot and object poses are collected into a joint variable set before applying the distributed solver.
- 4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations: The implementation detects objects from RGBD data, estimates their poses by segmenting and registering point clouds, and associates detections by category label and spatial proximity.A new landmark is created when no same-category object is found within the association distance.
- 4 Dealing With Bandwidth Constraints II: Compressing Sensor Data via Object-based Representations: At rendezvous, robots communicate detected-object labels and poses, after which object-object factors enforce a shared reference frame for future data association.Before the first rendezvous, each robot performs single-robot SLAM using wheel odometry and object-relative measurements.
5 Experiments
Experiments show that DGS provides accurate, scalable, noise-robust distributed trajectory estimation with low communication, while object-based maps substantially reduce memory requirements and support field mapping.
- 5 Experiments: DGS dominates the evaluated distributed algorithms in convergence speed and remains accurate, scalable, robust to noise, and communication-efficient.The experiments compare DGS with JOR, SOR, DJ, centralized methods, and DDF-SAM across simulations and field tests.
- 5 Experiments: SOR with γ = 1 converges faster than JOR and other tested SOR parameter choices across robot counts and measurement-noise levels.SOR with γ = 1 requires only a few tens of iterations in both rotation and pose subproblems.
- 5 Experiments: DGS has an anytime behavior: trajectory estimates are accurate after a few iterations and asymptotically converge to the centralized estimate.Individual robot errors need not decrease monotonically during convergence.
- 5 Experiments: Across increasing team sizes and noise levels, DGS errors become negligible within a few tens of iterations and replicate centralized accuracy.Larger teams or higher noise can produce slightly slower convergence or longer tails.
- 5 Experiments: DGS with relaxed stopping thresholds produces accurate estimates within few iterations even for large robot teams, with only a small accuracy loss.The loss becomes significant only in the 49-robot scenario.
- 5 Experiments: In field tests, DDF-SAM attains higher cost than DGS, while object-based maps reduce per-robot memory by orders of magnitude and support detected-object mapping.The field evaluation includes tests with two and four robots in indoor and mixed indoor-outdoor environments.
6 Conclusions and Future Work
The paper concludes that DGS provides an effective distributed approach for multi-robot trajectory estimation and object-based mapping, while identifying robustness, object-model, and platform-scope extensions.
- DGS offers accurate, noise-robust, privacy-preserving distributed trajectory estimation with low communication, few rounds, simple implementation, and scalability to large teams.Its effectiveness was demonstrated in extensive simulations and field tests.
- Object-based landmarks further reduce memory requirements and information exchange in multi-robot mapping, with demonstrations in Gazebo and a military test facility.
- The current object-based mapping approach assumes object models are known in advance, motivating future joint optimization of previously unknown object shapes.
- The nonlinear least-squares formulation is not robust to gross outliers, motivating algorithms that handle spurious measurements.
- Future evaluation will extend from large ground-robot teams toward flying robots and agile micro-aerial-vehicle swarms with limited resources.