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Automatic Map Density Selection for Locally-Performant Visual Place Recognition

Somayeh Hussaini, Tobias Fischer, Michael Milford

TL;DR

This paper proposes a dynamic VPR mapping approach that uses pairs of reference traverses from the target environment to automatically select an appropriate map density to satisfy two user-defined requirements: a target Local Recall@1 level and the proportion of the operational environment over which this requirement must be met or exceeded.

Abstract

A key challenge in translating Visual Place Recognition (VPR) from the lab to long-term deployment is ensuring a priori that a system can meet user-specified performance requirements across different parts of an environment, rather than just on average globally. A critical mechanism for controlling local VPR performance is the density of the reference mapping database, yet this factor is largely neglected in existing work, where benchmark datasets with fixed, engineering-driven (sensors, storage, GPS frequency) sampling densities are typically used. In this paper, we propose a dynamic VPR mapping approach that uses pairs of reference traverses from the target environment to automatically select an appropriate map density to satisfy two user-defined requirements: (1) a target Local Recall@1 level, and (2) the proportion of the operational environment over which this requirement must be met or exceeded, which we term the Recall Achievement Rate (RAR). Our approach is based on the hypothesis that match patterns between multiple reference traverses, evaluated across different map densities, can be modelled to predict the density required to meet these performance targets on unseen deployment data. Through extensive experiments across multiple VPR methods and the Nordland and Oxford RobotCar benchmarks, we show that our system consistently achieves or exceeds the specified local recall level over at least the user-specified proportion of the environment. Comparisons with alternative baselines demonstrate that our approach reliably selects the correct operating point in map density, avoiding unnecessary over-densification. Finally, ablation studies and analysis evaluate sensitivity to reference map choice and local space definitions, and reveal that conventional global Recall@1 is a poor predictor of the often more operationally meaningful RAR metric.

Automatic Map Density Selection for Locally-Performant Visual Place Recognition

TL;DR

This paper proposes a dynamic VPR mapping approach that uses pairs of reference traverses from the target environment to automatically select an appropriate map density to satisfy two user-defined requirements: a target Local Recall@1 level and the proportion of the operational environment over which this requirement must be met or exceeded.

Abstract

A key challenge in translating Visual Place Recognition (VPR) from the lab to long-term deployment is ensuring a priori that a system can meet user-specified performance requirements across different parts of an environment, rather than just on average globally. A critical mechanism for controlling local VPR performance is the density of the reference mapping database, yet this factor is largely neglected in existing work, where benchmark datasets with fixed, engineering-driven (sensors, storage, GPS frequency) sampling densities are typically used. In this paper, we propose a dynamic VPR mapping approach that uses pairs of reference traverses from the target environment to automatically select an appropriate map density to satisfy two user-defined requirements: (1) a target Local Recall@1 level, and (2) the proportion of the operational environment over which this requirement must be met or exceeded, which we term the Recall Achievement Rate (RAR). Our approach is based on the hypothesis that match patterns between multiple reference traverses, evaluated across different map densities, can be modelled to predict the density required to meet these performance targets on unseen deployment data. Through extensive experiments across multiple VPR methods and the Nordland and Oxford RobotCar benchmarks, we show that our system consistently achieves or exceeds the specified local recall level over at least the user-specified proportion of the environment. Comparisons with alternative baselines demonstrate that our approach reliably selects the correct operating point in map density, avoiding unnecessary over-densification. Finally, ablation studies and analysis evaluate sensitivity to reference map choice and local space definitions, and reveal that conventional global Recall@1 is a poor predictor of the often more operationally meaningful RAR metric.
Paper Structure (52 sections, 10 equations, 11 figures, 2 tables)

This paper contains 52 sections, 10 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Visual place recognition
  4. Sampling techniques in SLAM and robot navigation
  5. Reference database refinement methods for VPR
  6. Methodology
  7. Problem formulation
  8. Reference density sampling selector process
  9. Feature extraction for the recall@1 predictor
  10. Jump Rate ($x_{i,k,1}$)
  11. Fraction Outside Main Cluster ($x_{i,k,2}$)
  12. Largest Cluster Fraction ($x_{i,k,3}$)
  13. Turn Rate ($x_{i,k,4}$)
  14. Segment-wise recall at 1 predictor
  15. Sampling rate selection policy
  16. ...and 37 more sections

Figures (11)

  • Figure 1: Our approach models the relationship between local Recall@1 performance and factors extracted from analysis of variable density sampling of initial map data, in order to produce a final map density that ensures a user-specified local Recall@1 performance requirement is met or exceeded for a user-required percentage of local regions of the environment, dubbed the Recall Achievement Rate (RAR).
  • Figure 2: Overview of our automatic reference density selection framework. (A) Input reference traversals are segmented and processed at varying densities. (B) Features extracted from the (Ref1, Ref2) similarity matrix are used to train density-specific Ridge regressors that predict segment-wise Recall@1. (C) The selection policy identifies the sparsest sampling rate that satisfies the user-defined target recall achievement rate (RAR). (D) The optimised reference map is generated and evaluated against the unseen query traversal (Qry1).
  • Figure 3: The recall at 1 performance of the different reference density sampling rates (from dense (SR1: $k$=1) to sparsest (SR50: $k$=50)) on a per-segment basis from Cosplace on Nordland against the recall achievement rates, which is the percentage of segments whose per-segment R@1 is equal to or greater than the R@1 threshold (t).
  • Figure 4: Qualitative results: given the user-specified conditions, our approach selects a reference density sampling rate that meets the conditions using sparse sampling density. [Top] For a target R@1 of 60% and a target RAR of 40%, the segment's achieved R@1 is 60% using our approach with a sampling rate of 15. [Bottom] For a target R@1 of 80% and a target RAR of 60%, the segment's achieved R@1 is 90% with a sampling rate of 5.
  • Figure 5: The recall achievement rate (RAR) of the different sampling rates against the mean R@1 for different per-segment target R@1 of 20%, 40%, 60%, 80% and 100% from MixVPR on Nordland. Observations: the RAR metric is dependent on the per-segment target R@1 whereas the global mean R@1 is not. With increasing per-segment target R@1, the point for the same sampling density is pushed leftward, as less segments meet that stricter target R@1.
  • ...and 6 more figures