The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

Odometry is crucial for the navigation of autonomous vehicles in unknown environments. While cameras and LiDARs are commonly used to estimate the ego-motion of a vehicle, these sensors face limitations under bad lighting and severe weather conditions. Automotive radars overcome these challenges, but radar point clouds are generally sparse and noisy, making it difficult to identify useful features within a radar scan. In this paper, we address the problem of ego-motion estimation using a single automotive radar sensor. We propose a simple, yet effective, heuristic-based method to extract the ground plane from single radar scans and perform ground plane matching between consecutive scans. Additionally, we perform a windowed factor-graph optimization of the poses together with the ground plane, improving the accuracy of the pose estimation. We put our work to the test using the 4DRadarDataset. Our findings illustrate the state-of-the-art performance of our odometry approach compared to existing alternatives that use radar point clouds.

Odometry and mapping play a pivotal role in the navigation of autonomous vehicles. In this paper, we address the problem of pose estimation and map creation using only radar sensors. We focus on two odometry estimation approaches followed by a mapping step. The first one is a new point-to-point ICP approach that leverages the velocity information provided by 3D radar sensors. The second one is advantageous for 2D radars with a low number of samples, and particularly useful for scenarios where the sensor is being blocked by large dynamic obstacles. It exploits a constant velocity filter and the measured Doppler velocities to estimate the vehicle’s ego-motion. We enrich this with a filtering step to improve the accuracy of the points in the resulting map. We put our work to the test using the View of Delft and NuScenes datasets, which involve 3D and 2D radar sensors. Our findings illustrate state-of-the-art performance of our odometry techniques in terms of accuracy when compared to existing alternatives. Moreover, we demonstrate that our map filtering methodology achieves higher similarity rates than the raw unfiltered map when benchmarked against a corresponding LiDAR map.

Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving instance tracking in sparse radar point clouds to enhance scene interpretation. We propose a learning-based radar tracker incorporating temporal offset predictions to enable direct center-based association and enhance segmentation performance by including additional motion cues. We implement attention-based tracking for sparse radar scans to include appearance features and enhance performance. The final association combines geometric and appearance features to overcome the limitations of center-based tracking to associate instances reliably. Our approach shows an improved performance on the moving instance tracking benchmark of the RadarScenes dataset compared to the current state of the art.

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this article, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our radar instance transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation.

The awareness about moving objects in the surroundings of a self-driving vehicle is essential for safe and reliable autonomous navigation. The interpretation of LiDAR and camera data achieves exceptional results but typically requires to accumulate and process temporal sequences of data in order to extract motion information. In contrast, radar sensors, which are already installed in most recent vehicles, can overcome this limitation as they directly provide the Doppler velocity of the detections and, hence incorporate instantaneous motion information within a single measurement. In this paper, we tackle the problem of moving object segmentation in noisy radar point clouds. We also consider differentiating parked from moving cars, to enhance scene understanding. Instead of exploiting temporal dependencies to identify moving objects, we develop a novel transformer-based approach to perform single-scan moving object segmentation in sparse radar scans accurately. The key to our Radar Velocity Transformer is to incorporate the valuable velocity information throughout each module of the network, thereby enabling the precise segmentation of moving and non-moving objects. Additionally, we propose a transformer-based upsampling, which enhances the performance by adaptively combining information and over-coming the limitation of interpolation of sparse point clouds. Finally, we create a new radar moving object segmentation benchmark based on the RadarScenes dataset and compare our approach to other state-of-the-art methods. Our network runs faster than the frame rate of the sensor and shows superior segmentation results using only single-scan radar data.

Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many differences between their CNN configurations, which do not provide insights in how fine-grade layer design choices affect this balance.We present BackboneAnalysis, a methodology for extracting structured insights into the trade-off for a chosen target compute platform. Within a one-factor-at-a-time analysis setup, CNN architectures are systematically varied and evaluated based on throughput and latency measurements irrespective of model accuracy. Thereby, we investigate the configuration factors input shape, batch size, kernel size and convolutional layer type.In our experiments, we deploy BackboneAnalysis on a Xavier iGPU and a Coral Edge TPU accelerator. The analysis reveals that the general assumption from optimal Roofline performance that higher operation density in CNNs leads to higher throughput does not always hold. These results highlight the importance for a neural network architect to be aware of platform-specific latency and throughput behavior in order to derive sensible configuration decisions for a custom CNN.