Conference Agenda

This paper presents a radar-centric multi-sensor fusion framework, RLIO, designed for robust indoor SLAM in perceptually challenging environments such as underground garages and smoke-filled areas. Unlike conventional LiDAR-based methods that degrade under poor visibility, RLIO tightly integrates 4D imaging radar, 3D LiDAR, and an IMU within an iterated extended Kalman filter. The system introduces three key modules: a motion-prior-driven radar velocimetry algorithm for stable velocity estimation, a velocity-prior-enhanced scan-to-map registration for drift reduction in degenerate geometries, and an adaptive fusion strategy that dynamically adjusts sensor weights based on real-time degradation detection. Experimental results from both handheld and UGV platforms demonstrate that RLIO achieves accurate localization and high-quality mapping even when LiDAR performance deteriorates due to smoke or repetitive structures, highlighting its potential for reliable all-weather autonomous navigation and mapping in complex indoor and outdoor environments.

4D radars have attracted increasing interest for robotic perception because they remain effective in adverse conditions such as darkness, dust, smoke, rain, and fog. Compared with conventional automotive radars that mainly provide planar coordinates and relative Doppler velocity, modern 4D radars also sense elevation, which makes them more suitable for geometric odometry and mapping.

In this paper, we propose RAMBA, an offline 4D radar mapping framework based on bundle adjustment. Starting from initial poses and radar frames produced by a radar--inertial odometry front-end, we refine the radar frame states to improve global mapping consistency, measured by covariance-weighted point-to-point distances. In essence, our method extends pairwise generalized iterative closest point (GICP) to the multi-frame setting. Candidate correspondences are formed within voxels of a voxel grid built from all selected frames, and each residual is weighted by the sum of the two point covariances. The geometric constraints are jointly optimized with IMU preintegration and radar ego-velocity constraints.

To reduce false associations caused by drift and revisits, RAMBA enforces temporal consistency when forming correspondences and explicitly allows constraints around loop closures. We evaluate the method on the ColoRadar and SNAIL Radar datasets. The proposed refinement consistently improves map quality and usually improves trajectory accuracy over the initial radar--inertial odometry and pose graph optimization. To the best of our knowledge, this is the first geometric offline bundle-adjustment framework for consistent 4D radar mapping.

Four-dimensional (4D) imaging radar offers robustness in adverse weather and lighting, but its point clouds remain sparse, noisy, and affected by ghost reflections, making geometric scan matching unstable. This work integrates two existing deep correspondence models—Radar Transformer and RPM-Net—into a radar–inertial odometry pipeline without retraining. Both networks run asynchronously in dedicated ROS nodes: radar and submap point clouds are cropped, transformed, and sent to the matchers, which return either hard or soft correspondences for the first IEKF iteration of each frame. When neural outputs are delayed, the system automatically falls back to geometric matching.

Returned matches are fused with a voxelized IEKF backend that computes Mahalanobis-weighted residuals. RPM-Net further supplies soft targets and confidence weights, enhancing point-to-point constraints. Experiments on ColoRadar indoor and outdoor sequences show that learning-based correspondences can reduce drift in weakly structured scenes while maintaining robustness when geometry is reliable.

This paper presents MSMR SLAM, a multi sensor and multi robot SLAM framework that integrates LiDAR, 4D radar, and IMU to achieve robust localization and consistent mapping in large scale and degraded environments. The front end includes radar inertial odometry and LiDAR inertial odometry, and evaluates the reliability of LiDAR observations using point cloud sparsity and effective range. An adaptive fusion module combines the two odometry estimates to maintain stable state estimation, while radar assisted dynamic point removal improves the reliability of geometric constraints. The back end constructs a unified factor graph that incorporates multi sensor odometry constraints, loop closure factors, and inter robot association factors. A LiDAR centered and radar assisted matching strategy enhances cross robot data association, and radar based loop closures improve global consistency when LiDAR measurements degrade. The system maintains a dense LiDAR map together with a complementary radar map, enabling hybrid mapping that remains reliable in perceptually challenging regions. Experiments on campus datasets, including smoke filled scenarios, demonstrate that MSMR SLAM achieves high precision multi robot localization and globally consistent mapping. Compared with single robot baselines, the proposed framework provides improved accuracy and robustness, and the integration of LiDAR and radar yields more complete and stable map reconstruction in complex environments.

1. We introduce a novel framework for 4D radar-camera extrinsic calibration that utilizes commonly available dynamic objects as natural correspondences.

2. We develop a heuristic-guided strategy to reliably associate radar points with image detections and estimate the extrinsic parameters without deep learning.