Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

Legged robots traverse unstructured terrain through brief, intermittent foot–ground contacts whose support conditions are difficult to perceive and predict in real time. In such regimes, haptic feedback provides early and trustworthy evidence of traction limits, partial support, and incipient slip. This structured survey asks two questions: first, what locomotion-relevant contact evidence can be acquired and preserved under real deployment constraints; and second, how that evidence is translated into controller-facing products and consumed under uncertainty and safety constraints. Using a PRISMA-informed search methodology, we organise the literature into three complementary sensing channels—D1 interface fields (pressure/shear and contact geometry), D2 near-foot wrenches and vibro-acoustic transients, and D3 proprioceptive inference through joint and body dynamics—and into three controller-facing feedback targets—F1 contact events and modes, F2 realised interaction state (wrench/CoP), and F3 feasibility-envelope updates with uncertainty. For each channel we summarise transduction mechanisms, representative implementations, validation maturity, and learning-based representations that yield task-sufficient products under latency, bandwidth, and durability constraints. We then synthesise how these products enter estimation, planning, and control across model-based, hybrid, and policy-centric pipelines (A/B/C), and how safety overlays such as feasibility-aware optimisation, uncertainty-aware constraint tightening, and barrier-/shield-based run-time filters consume them. Building on this synthesis, we offer an information-centred perspective that interprets each footfall as an action-conditioned update over latent contact and terrain variables, using this lens to discuss the outlook of controller-oriented sensor layout, morphology–sensing–representation co-design, active haptic exploration, and information-health monitoring under long-term deployment.