Abstract
Simultaneous localization and mapping (SLAM) plays a vital role in mapping unknown spaces and aiding autonomous navigation. Virtually all state-of-the-art solutions today for 2D SLAM are designed for dense and accurate sensors such as laser range-finders (LiDARs). However, these sensors are not suitable for resource-limited nano robots, which become increasingly capable and ubiquitous nowadays, and these robots tend to mount economical and low-power sensors that can only provide sparse and noisy measurements. This introduces a challenging problem called SLAM with sparse sensing. This work addresses the problem by adopting the form of the state-of-the-art graph-based SLAM pipeline with a novel frontend and an improvement for loop closing in the backend, both of which are designed to work with sparse and uncertain range data. Experiments show that the maps constructed by our algorithm have superior quality compared to prior works on sparse sensing. Furthermore, our method is capable of running in real-time on a modern PC with an average processing time of 1/100th the input interval time.
Overview
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The paper addresses the challenges of applying SLAM to robots with limited sensing capabilities, focusing on small drones.
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A graph-based SLAM pipeline is adapted with a new frontend and enhanced backend for processing sparse and noisy sensor data.
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A novel 'landmark graph' approach and an approximate match heuristic for loop closure detection are the key innovations of the proposed system.
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Extensive experimentation shows the algorithm's superior map quality and real-time performance even with sparse data.
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The research advances SLAM applications for resource-constrained robots, enabling exploration in unknown territories where dense sensors are impractical.
Introduction to Graph SLAM with Sparse Sensing
Simultaneous localization and mapping (SLAM) is a key process that allows robots to understand and navigate their environment by determining their position and creating a map at the same time. SLAM is particularly useful for guiding robots through unknown territories. Typically, SLAM relies on dense and accurate sensor data, like that from Light Detection and Ranging (LiDAR) systems. However, certain applications, such as using small nano drones for exploration, require a different approach due to their limited sensor capabilities.
Tackling Sparse Sensing
When it comes to smaller robots, like nano drones, the limited sensor data poses a significant challenge to traditional SLAM approaches, as these are designed to work with rich, detailed sensory input. These smaller robots operate with sparse and noisy data, which demands a new method of SLAM adapted to these limitations. This research presents an innovative solution by adopting a state-of-the-art graph-based SLAM pipeline, integrating a novel frontend for data processing and enhancing loop closure detection in the backend. The resulting maps from this algorithm have been shown to be of superior quality compared to previous methods designed for sparse sensing.
Key Innovations of the Proposed SLAM System
The researchers propose:
- A new open-source graph-based approach to solve SLAM with sparse sensing, capable of real-time performance on a standard modern computer.
- A novel "landmark graph" that takes place of scan-matching as the frontend for handling sparse range data, forming hypotheses of pose-to-pose and pose-to-landmark relations.
- An approximate match heuristic applied to the existing scan-to-map matching algorithm, which is designed to be robust against sparse and noisy data, simplifying the loop closure process and correcting mismatches effectively.
Evaluating the Algorithm
The effectiveness of this algorithm is demonstrated through extensive experiments, using both sub-sampling on established datasets and real-world data collected from nano drones equipped with limited-range sensors. In comparison to other methods, including GMapping which requires denser range measurements, the proposed algorithm successfully yields comparable or better map quality with far fewer measurements. Also, the speed evaluation indicates that the algorithm is not only accurate but also quick, utilizing significantly less processing time than traditional methods.
Conclusion
The presented research marks significant progress in the domain of SLAM for robots with sparse sensing capabilities. Its real-time processing ability, robustness to sparse data, and reduced computational demands make it a considerable advancement for mapping unknown spaces, particularly for resource-constrained robots. This work opens up new possibilities for utilizing small drones and similar devices in complex exploration tasks where traditional, sensor-heavy approaches are impractical.
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