Learning Place-and-Time-Dependent Binary Descriptors for Long-Term Visual Localization

Vision-based navigation is extremely susceptible to natural scene changes. This can result in localization failures in less than a few hours after map creation. To combat short-term illumination changes as well as long-term seasonal variations, we propose using a place-and-time-dependent binary descriptor that adapts to different scenarios in an online fashion. This is achieved by extending the GRIEF [6] evolution algorithm in two ways: correspondence generation using a known pose change and the inclusion of LATCH triplets in addition to BRIEF comparisons for descriptor generation. We show the adaptive descriptor outperforms a single descriptor scheme for localization within a single-experience Visual Teach and Repeat (VT&R) system while maintaining the efficiency of binary descriptors. By adapting the description function to different environmental conditions, it allows the system to operate for a longer period before a new experience is required. In the presence of extreme illumination changes from day to night, we obtain 40% more inlier matches compared to SURF. In the case of seasonal variations, a 70% increase is demonstrated. The increased correspondences result in more localizable sections along the paths, amounting to a 25% and 150% increase in the lighting and seasonal cases, respectively.

Authors

• Nan Zhang
• Michael Warren
• Tim D. Barfoot

Keywords

• Lighting
• Visualization
• Latches
• Navigation
• Robustness
• Measurement
• Evolutionary computation
• Binary Descriptors
• Seasonal Variation
• Evolutionary Algorithms
• Natural Scenes
• Illumination Changes
• Scene Changes
• Creation Of Maps
• Training Data
• Support Vector Machine
• Live Imaging
• Seasonal Changes
• Unmanned Aerial Vehicles
• Light Detection And Ranging
• Feature Points
• Labeled Data
• Feature Matching
• Vertices
• Image Descriptors
• Matching Performance
• Fitness Score
• Random Sample Consensus
• Grizzly
• Bundle Adjustment
• Visual Odometry
• Viewpoint Changes