Dr-PoGO: Direct Radar Pose-Graph Optimization

• URL: http://arxiv.org/abs/2605.04806v1
• Date: Wed, 06 May 2026 11:58:39 GMT
• Title: Dr-PoGO: Direct Radar Pose-Graph Optimization
• Abstract summary: This paper introduces Dr-PoGO, a method for Simultaneous Localization And Mapping (SLAM) using a 2D spinning radar.<n>Unlike cameras or lidars that require line-of-sight, millimetre-wave radars can see' through dust, falling snow, rain, etc.<n>Dr-PoGO demonstrates state-of-the-art performance over 300km of data in various real-world automotive environments.
• Score: 11.790199751127574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces Dr-PoGO, a method for Simultaneous Localization And Mapping (SLAM) using a 2D spinning radar. Unlike cameras or lidars that require line-of-sight, millimetre-wave radars can `see' through dust, falling snow, rain, etc. Accordingly, it is a great modality for robust perception regardless of the weather conditions. While most existing radar-based SLAM methods rely on the extraction of point clouds or features to perform ego-motion estimation, Dr-PoGO leverages direct registration techniques for odometry (DRO) and loop-closure registration. An off-the-shelf radar-focused place recognition algorithm, RaPlace, provides loop-closure candidates. As RaPlace does not provide relative transformations, Dr-PoGO introduces a coarse-to-fine registration that uses visual features and descriptors to obtain an initial guess for the direct transformation refinement. The global trajectory is optimized in a pose-graph optimization. Dr-PoGO demonstrates state-of-the-art performance over 300km of data in various real-world automotive environments. Our implementation is publicly available: https://github.com/utiasASRL/dr_pogo.

Related papers

• FD-SLAM: Fast Dense Radar-Inertial SLAM with Frequency-Domain Loop Closure and Pose Graph Optimization [0.0]
  This paper presents FD-SLAM, a fast dense radar-inertial SLAM system that extends dense radar-inertial odometry with frequency-domain loop closure and pose graph optimization.<n>The results show that FD-SLAM improves FD-RIO baseline, achieves competitive performance against current state-of-the-art radar SLAM methods, and provides favorable rotational accuracy across multiple driving trajectories.
  arXiv  Detail & Related papers  (2026-06-13T22:01:48Z)
• RadarSim: Simulating Single-Chip Radar via Multimodal Neural Fields [49.41438378775056]
  Radars are an ideal complement to cameras, with cameras offering fine angular resolution and radars providing metric depth and robustness under adverse weather.<n>RadarSim produces sharper geometry and Doppler range frames than radar-only reconstructions.
  arXiv  Detail & Related papers  (2026-05-25T21:03:40Z)
• Dr-BA: Separable Optimization for Direct Radar Bundle Adjustment & Localization [12.318856176227383]
  Dr-BA is a first-of-its-kind radar bundle adjustment framework that operates directly on 2D spinning radar intensity images.<n>Dr-BA achieves state-of-the-art radar-based BA and cross-session localization performance, demonstrated on more than 200 km of on-road data.
  arXiv  Detail & Related papers  (2026-05-07T23:41:16Z)
• RadarSplat-RIO: Indoor Radar-Inertial Odometry with Gaussian Splatting-Based Radar Bundle Adjustment [15.74169173366249]
  Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM)<n>Most radar SLAM pipelines still rely heavily on frame-to-frame odometry, which leads to substantial drift.<n>We present the first radar bundle adjustment (BA) framework enabled by Gaussian Splatting (GS), a dense and differentiable representation.
  arXiv  Detail & Related papers  (2026-04-15T05:26:46Z)
• RadarGen: Automotive Radar Point Cloud Generation from Cameras [64.69976771710057]
  We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery.<n>RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form.<n>We show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data.
  arXiv  Detail & Related papers  (2025-12-19T18:57:33Z)
• DRO: Doppler-Aware Direct Radar Odometry [11.042292216861762]
  A renaissance in radar-based sensing for mobile robotic applications is underway.<n>We propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars.<n>Our method has been validated on over 250km of on-road data sourced from public datasets.
  arXiv  Detail & Related papers  (2025-04-29T01:20:30Z)
• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion.<n>Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed.<n>Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection [68.99784784185019]
  Poor lighting or adverse weather conditions degrade camera performance.<n>Radar suffers from noise and positional ambiguity.<n>We propose RobuRCDet, a robust object detection model in BEV.
  arXiv  Detail & Related papers  (2025-02-18T17:17:38Z)
• RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar [15.776076554141687]
  3D occupancy-based perception pipeline has significantly advanced autonomous driving. Current methods rely on LiDAR or camera inputs for 3D occupancy prediction. We introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-05-22T21:48:17Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• DART: Implicit Doppler Tomography for Radar Novel View Synthesis [9.26298115522881]
  DART is a Neural Radiance Field-inspired method which uses radar-specific physics to create a reflectance and transmittance-based rendering pipeline for range-Doppler images. In comparison to state-of-the-art baselines, DART synthesizes superior radar range-Doppler images from novel views across all datasets.
  arXiv  Detail & Related papers  (2024-03-06T17:54:50Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.