F$^3$Loc: Fusion and Filtering for Floorplan Localization
(2403.03370)Abstract
In this paper we propose an efficient data-driven solution to self-localization within a floorplan. Floorplan data is readily available, long-term persistent and inherently robust to changes in the visual appearance. Our method does not require retraining per map and location or demand a large database of images of the area of interest. We propose a novel probabilistic model consisting of an observation and a novel temporal filtering module. Operating internally with an efficient ray-based representation, the observation module consists of a single and a multiview module to predict horizontal depth from images and fuses their results to benefit from advantages offered by either methodology. Our method operates on conventional consumer hardware and overcomes a common limitation of competing methods that often demand upright images. Our full system meets real-time requirements, while outperforming the state-of-the-art by a significant margin.
Overview
-
F$3$Loc introduces a lightweight, probabilistic model for indoor localization within floorplans, leveraging single and multi-view imagery without the need for upright images or extensive computing resources.
-
The framework combines data-driven observation with a selection network and an SE2 histogram filter for efficient floorplan localization on consumer hardware.
-
It addresses scale ambiguity and refines localization over time through a novel integration of depth cues and temporal filtering.
-
F$3$Loc demonstrates significant improvements in speed, accuracy, and practicality for indoor navigation, with potential applications in AR/VR and autonomous robotics.
Efficient Data-Driven Localization within Floorplans Using Fusion, Filtering, and Consumer Hardware
Introduction
Camera localization within known environments has been a longstanding challenge in both the computer vision and robotics communities. Traditional approaches rely heavily on pre-existing databases or 3D models, which can be cumbersome in terms of storage and maintenance. Given the ubiquity of floorplans in indoor spaces, leveraging them for camera localization presents a promising, lightweight alternative. This paper introduces F$3$Loc: a novel, probabilistic model for efficient floorplan localization. Eschewing the need for upright images and heavyweight computing resources, F$3$Loc combines single and multi-view imagery with a novel temporal filtering approach, running on conventional consumer hardware.
The F$3$Loc Framework
The proposed F$3$Loc system consists of several key components designed to address the challenges of localizing within a floorplan. These include a data-driven observation model that integrates single and multi-view depth predictions, a selection network to fuse these cues based on their relative strengths, and an efficient SE2 histogram filter for temporal integration.
- Single Image Localization: Utilizing a combination of ResNet and Attention-based networks, F$3$Loc extracts depth from single images, aligning them with gravity direction to predict floorplan depth. This component helps tackle scale ambiguity common in monocular depth estimation.
- Multiview Stereo Estimation: Taking advantage of multiple views, F$3$Loc employs a variant of the MVS network to capture geometric cues, fundamentally improving depth estimation. This approach excels where there is sufficient baseline and image overlap but struggles with small baselines and near-in-place motion.
- Complementary Cue Selection: Realizing the unique advantages and disadvantages of single and multi-view cues, F$3$Loc incorporates a selection network that intelligently combines the two based on their relevance to the current situation. This facilitates the leveraging of either method's strengths as required.
- Temporal Localization: To refine localization over time and resolve ambiguities, F$3$Loc integrates single-frame predictions using a novel SE2 histogram filter. This efficient algorithm maintains a probability distribution over poses and makes use of known ego-motion to update these probabilities robustly.
- Robustness to Non-Upright Images: Addressing the practical limitation of requiring upright images, F$3$Loc introduces a virtual roll-pitch augmentation technique for its training process. This significantly enhances the model's robustness to varied camera orientations, aligning with practical usage scenarios more closely.
Practical Implications and Future Outlook
F$3$Loc sets a new standard for indoor localization against a floorplan, outperforming leading methods in rapidity, accuracy, and practical viability. By running efficiently on consumer hardware and accommodating non-upright images, it demonstrates superior real-world applicability.
This research not only contributes a robust solution to the floorplan localization challenge but also opens avenues for future work, particularly in the incorporation of semantic cues and the development of real-world datasets for further validation.
Considering the system's potential for broad application in augmented reality (AR), virtual reality (VR), and robotics, F$3$Loc represents a significant step forward. Its methodology supports the vision of creating more intuitive indoor navigation systems and autonomous exploration and rescue robots capable of operating in complex environments reliably.
Looking ahead, the integration of semantic information and the improvement of dataset diversity and realism stand out as promising directions. As indoor localization technology continues to evolve, systems like F$3$Loc pave the way for a future where digital intelligence seamlessly navigates and understands the physical world.
Conclusion
In summary, F$3$Loc introduces an innovative, probabilistic model for efficient and accurate indoor localization within floorplans, leveraging fusion and filtering techniques to operate effectively on consumer hardware. This system's adaptability to non-upright images and its combination of single and multi-view depth cues for real-time localization mark a noteworthy advancement in the field, promising enhanced capabilities for AR/VR applications and autonomous indoor navigation.
Create an account to read this summary for free: