Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry (2011.06838v3)

Abstract: We present an efficient multi-sensor odometry system for mobile platforms that jointly optimizes visual, lidar, and inertial information within a single integrated factor graph. This runs in real-time at full framerate using fixed lag smoothing. To perform such tight integration, a new method to extract 3D line and planar primitives from lidar point clouds is presented. This approach overcomes the suboptimality of typical frame-to-frame tracking methods by treating the primitives as landmarks and tracking them over multiple scans. True integration of lidar features with standard visual features and IMU is made possible using a subtle passive synchronization of lidar and camera frames. The lightweight formulation of the 3D features allows for real-time execution on a single CPU. Our proposed system has been tested on a variety of platforms and scenarios, including underground exploration with a legged robot and outdoor scanning with a dynamically moving handheld device, for a total duration of 96 min and 2.4 km traveled distance. In these test sequences, using only one exteroceptive sensor leads to failure due to either underconstrained geometry (affecting lidar) or textureless areas caused by aggressive lighting changes (affecting vision). In these conditions, our factor graph naturally uses the best information available from each sensor modality without any hard switches.

• The paper presents a unified multi-modal factor graph that fuses lidar, visual, and inertial data to enhance odometry performance.
• It introduces efficient 3D feature extraction from lidar data and passive frame synchronization with cameras to support real-time operation.
• Extensive tests in varied environments demonstrated robust navigation, covering 2.4 km and 96 minutes of operation even in challenging conditions.

Overview of Unified Multi-Modal Landmark Tracking for Lidar-Visual-Inertial Odometry

The paper "Unified Multi-Modal Landmark Tracking for Tightly Coupled Lidar-Visual-Inertial Odometry" introduces an innovative system that integrates lidar, visual, and inertial data into a single, efficient odometry framework. This research aims to enhance mobile platform navigation by leveraging a tightly integrated factor graph, which optimizes these sensor modalities in real-time. The system overcomes challenges in environments with poor geometric constraints or extreme lighting that traditionally impair odometry systems relying on a solitary sensor modality.

Methodology

The authors present a method to extract three-dimensional line and planar primitives from lidar data, which are crucial for tracking landmarks over multiple frame sequences. This contrasts with conventional methods that rely on frame-to-frame matching, leading to suboptimal performance in challenging scenarios. A noteworthy component of the system is the passive synchronization of frames between lidar and cameras, thereby allowing for seamless integration within the factor graph. This enables real-time operation on a single CPU, which is both lightweight and efficient, supporting use on various mobile platforms including legged robots and handheld devices.

Experimental Validation

The system was tested under rigorous conditions including underground exploration tasks and dynamic outdoor environments. Across these scenarios, the research highlights over 96 minutes of testing and 2.4 kilometers of movement. In these trials, reliance on a single sensor modality often led to failures due to insufficient geometric constraints for lidar or ineffective vision processing in poor lighting. However, the multi-modal approach of this system naturally balances the available sensor information without requiring manual sensor switching, demonstrating robust odometry even when individual modalities are compromised.

Key Contributions

Three main contributions mark the significance of this work:

• Novel Factor Graph Formulation: A cohesive factor graph that comprehensively fuses vision, lidar, and IMU measurements, achieving an integrated optimization framework that enhances robustness against sensor-specific shortcomings.
• Efficient Lidar Feature Extraction: The introduction of a method for extracting lidar feature landmarks treated in a homogeneous manner alongside visual features, significantly improving the real-time operational capabilities.
• Comprehensive Experimental Evaluation: Through extensive testing across various platforms and environmental conditions, the system demonstrated superior resilience and performance compared to common methodologies that fail under similar constraints.

Implications and Future Work

The implications of this research are profound, particularly in the development of autonomous systems requiring robust navigation capabilities in varied and complex environments. The integration of tightly coupled multi-modal sensor data into a coherent framework exemplifies a significant step in improving odometry efficiency and reliability.

Future developments could focus on exploring additional sensor modalities or enhancing the system’s adaptability to even more disparate environmental conditions. Furthermore, the scalability of the system in large-scale robotic platforms or its integration with machine learning approaches for improved feature detection and tracking could be potential avenues of exploration.

In conclusion, this paper contributes meaningfully to the field of robotics and autonomous navigation by demonstrating the advantages of tightly coupled multi-modal sensor integration over conventional loosely coupled approaches. The resultant system is likely to influence future designs in odometry systems that demand high fidelity and adaptability across various operational domains.