MakeWay: Object-Aware Costmaps for Proactive Indoor Navigation Using LiDAR

TL;DR

This paper presents a LiDAR-based indoor navigation system that uses object-aware costmaps and a novel labeling technique to improve proactive collision avoidance and navigation safety in real-time.

Contribution

The paper introduces a new object-aware affordance-based costmap system and an automated labeling method for indoor LiDAR data, enhancing proactive navigation capabilities.

Findings

Effective object detection and tracking in LiDAR data.

Improved collision avoidance in indoor navigation.

Real-time operation demonstrated on robot platforms.

Abstract

In this paper, we introduce a LiDAR-based robot navigation system, based on novel object-aware affordance-based costmaps. Utilizing a 3D object detection network, our system identifies objects of interest in LiDAR keyframes, refines their 3D poses with the Iterative Closest Point (ICP) algorithm, and tracks them via Kalman filters and the Hungarian algorithm for data association. It then updates existing object poses with new associated detections and creates new object maps for unmatched detections. Using the maintained object-level mapping system, our system creates affordance-driven object costmaps for proactive collision avoidance in path planning. Additionally, we address the scarcity of indoor semantic LiDAR data by introducing an automated labeling technique. This method utilizes a CAD model database for accurate ground-truth annotations, encompassing bounding boxes, positions, orientations, and point-wise semantics of each object in LiDAR sequences. Our extensive evaluations, conducted in both simulated and real-world robot platforms, highlights the effectiveness of proactive object avoidance by using object affordance costmaps, enhancing robotic navigation safety and efficiency. The system can operate in real-time onboard and we intend to release our code and data for public use.